Project Objective

Home Credit Group, founded in Czech Republic a d headquartered in Netherlands, is a multinational non-bank financial company that provides consumer financial products, such as personal lending and credit card businesses. The company focuses primarily on people with no or little credit history. The project objective is to interpret how the information on the loan applications could affect the default risks of loan applicants.

Variable Descriptions

  • TARGET: Target variable (1 - client with payment difficulties: he/she had late payment more than X days on at least one of the first Y installments of the loan in our sample, 0 - all other cases)
  • CODE_GENDER: Gender of the client
  • NAME_CONTRACT_TYPE: Identification if loan is cash or revolving
  • AMT_INCOME_TOTAL: Income of the client
  • FLAG_OWN_CAR: Flag if the client owns a car
  • FLAG_OWN_REALTY: Flag if client owns a house or flat
  • CNT_CHILDREN: Flag if client owns a house or flat
  • AMT_CREDIT: Credit amount of the loan
  • AMT_ANNUITY: Loan annuity
  • AMT_GOODS_PRICE: For consumer loans it is the price of the goods for which the loan is given
  • NAME_TYPE_SUITE: Who was accompanying client when he was applying for the loan
  • NAME_INCOME_TYPE: Clients income type (businessman, working, maternity leave,…)
  • NAME_EDUCATION_TYPE: Level of highest education the client achieved
  • NAME_FAMILY_STATUS: Family status of the client
  • NAME_HOUSING_TYPE: What is the housing situation of the client (renting, living with parents, …)
  • REGION_POPULATION_RELATIVE: Normalized population of region where client lives (higher number means the client lives in more populated region)
  • DAYS_BIRTH: Client’s age in days at the time of application
  • DAYS_EMPLOYED: How many days before the application the person started current employment
  • OWN_CAR_AGE: Age of client’s car
  • OCCUPATION_TYPE: What kind of occupation does the client have
  • CNT_FAM_MEMBERS: How many family members does client have
  • ORGANIZATION_TYPE: Type of organization where client works
  • AMT_REQ_CREDIT_BUREAU_DAY: Number of enquiries to Credit Bureau about the client one day before application (excluding one hour before application)
  • AMT_REQ_CREDIT_BUREAU_WEEK: Number of enquiries to Credit Bureau about the client one week before application (excluding one day before application)
  • AMT_REQ_CREDIT_BUREAU_MON: Number of enquiries to Credit Bureau about the client one month before application (excluding one week before application)
  • AMT_REQ_CREDIT_BUREAU_QRT: Number of enquiries to Credit Bureau about the client 3 month before application (excluding one month before application)
  • AMT_REQ_CREDIT_BUREAU_YEAR: Number of enquiries to Credit Bureau about the client one day year (excluding last 3 months before application)

Load Data and Data Pre-processing

As the original dataset contains over 200 independent variables, I picked the 23 variables for further analysis and interpretations based on my interest and work experience in this industry. Besides, I also added the following 6 variables together to improve the interpretability of my model: AMT_REQ_CREDIT_BUREAU_HOUR, AMT_REQ_CREDIT_BUREAU_DAY, AMT_REQ_CREDIT_BUREAU_WEEK, AMT_REQ_CREDIT_BUREAU_MON, AMT_REQ_CREDIT_BUREAU_QRT, AMT_REQ_CREDIT_BUREAU_YEAR. These variables represent the number of enquiries to Credit Bureau about the client one hour, day(excluding one hour before), week(excluding one day before), month(excluding one week before), quarter(excluding one month before) and year(excluding one quarter before) before application. As my goal is to construct a interpretation model, instead of a predictive model, it will enhance the interpretability of the model substantially by adding them together. On the other hand, if I intended to build a predictive model, I should keep them as 5 different variables.

#select interested variables
application_data_selected <- subset(application_data, select = c("TARGET","CODE_GENDER","NAME_CONTRACT_TYPE","AMT_INCOME_TOTAL","FLAG_OWN_CAR","FLAG_OWN_REALTY","CNT_CHILDREN","AMT_CREDIT", "AMT_ANNUITY", "AMT_GOODS_PRICE", "NAME_TYPE_SUITE","NAME_INCOME_TYPE","NAME_EDUCATION_TYPE","NAME_FAMILY_STATUS","NAME_HOUSING_TYPE","REGION_POPULATION_RELATIVE","DAYS_BIRTH","DAYS_EMPLOYED","OWN_CAR_AGE","OCCUPATION_TYPE","CNT_FAM_MEMBERS","ORGANIZATION_TYPE","AMT_REQ_CREDIT_BUREAU_HOUR","AMT_REQ_CREDIT_BUREAU_DAY","AMT_REQ_CREDIT_BUREAU_WEEK","AMT_REQ_CREDIT_BUREAU_MON","AMT_REQ_CREDIT_BUREAU_QRT","AMT_REQ_CREDIT_BUREAU_YEAR"))

#combine all the AMT_REQ_CREDIT_BUREAU variables together to compute the aggregated
#number of enquiries to Credit Bureau about the client one year before the application
application_data_selected$AMR_REQ_CREDIT_BUREAU_SUM <- application_data_selected$AMT_REQ_CREDIT_BUREAU_HOUR+application_data_selected$AMT_REQ_CREDIT_BUREAU_DAY+application_data_selected$AMT_REQ_CREDIT_BUREAU_MON+application_data_selected$AMT_REQ_CREDIT_BUREAU_QRT+application_data_selected$AMT_REQ_CREDIT_BUREAU_WEEK+application_data_selected$AMT_REQ_CREDIT_BUREAU_YEAR

#Drop AMT_REQ_CREDIT_BUREAU_HOUR, DAY, WEEK, MON, QRT, YEAR
application_data_selected <- application_data_selected %>%
    dplyr::select(-(AMT_REQ_CREDIT_BUREAU_HOUR)) %>%
    dplyr::select(-(AMT_REQ_CREDIT_BUREAU_DAY)) %>%
    dplyr::select(-(AMT_REQ_CREDIT_BUREAU_WEEK)) %>%
    dplyr::select(-(AMT_REQ_CREDIT_BUREAU_MON)) %>%
    dplyr::select(-(AMT_REQ_CREDIT_BUREAU_QRT)) %>%
    dplyr::select(-(AMT_REQ_CREDIT_BUREAU_YEAR))
#check collinearity
num <- unlist(lapply(application_data_selected, is.numeric))
cor(cc(application_data_selected[,num]), use = "pair")
##                                  TARGET AMT_INCOME_TOTAL CNT_CHILDREN
## TARGET                      1.000000000     -0.022374536  0.005679723
## AMT_INCOME_TOTAL           -0.022374536      1.000000000  0.006490665
## CNT_CHILDREN                0.005679723      0.006490665  1.000000000
## AMT_CREDIT                 -0.035478230      0.324854819 -0.021083017
## AMT_ANNUITY                -0.015778897      0.411376003 -0.001362760
## AMT_GOODS_PRICE            -0.044389541      0.332695445 -0.024611028
## REGION_POPULATION_RELATIVE -0.036110371      0.167362370 -0.032117581
## DAYS_BIRTH                  0.054896182      0.015586880  0.279672331
## DAYS_EMPLOYED              -0.028323415     -0.103847372 -0.183114593
## OWN_CAR_AGE                 0.037232346     -0.132171880  0.008950126
## CNT_FAM_MEMBERS            -0.001250596     -0.001726251  0.914413945
## AMR_REQ_CREDIT_BUREAU_SUM   0.019143211      0.035277693 -0.029676739
##                             AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE
## TARGET                     -0.03547823 -0.01577890     -0.04438954
## AMT_INCOME_TOTAL            0.32485482  0.41137600      0.33269544
## CNT_CHILDREN               -0.02108302 -0.00136276     -0.02461103
## AMT_CREDIT                  1.00000000  0.74634289      0.98718062
## AMT_ANNUITY                 0.74634289  1.00000000      0.75161981
## AMT_GOODS_PRICE             0.98718062  0.75161981      1.00000000
## REGION_POPULATION_RELATIVE  0.09204365  0.11265566      0.09609678
## DAYS_BIRTH                 -0.11694218 -0.05319892     -0.11380798
## DAYS_EMPLOYED              -0.01937100 -0.05245937     -0.01794351
## OWN_CAR_AGE                -0.09280021 -0.09619776     -0.10185298
## CNT_FAM_MEMBERS             0.02020290  0.03046515      0.01779781
## AMR_REQ_CREDIT_BUREAU_SUM  -0.01731215  0.01144510     -0.01839987
##                            REGION_POPULATION_RELATIVE   DAYS_BIRTH
## TARGET                                   -0.036110371  0.054896182
## AMT_INCOME_TOTAL                          0.167362370  0.015586880
## CNT_CHILDREN                             -0.032117581  0.279672331
## AMT_CREDIT                                0.092043647 -0.116942177
## AMT_ANNUITY                               0.112655663 -0.053198919
## AMT_GOODS_PRICE                           0.096096778 -0.113807976
## REGION_POPULATION_RELATIVE                1.000000000 -0.038847969
## DAYS_BIRTH                               -0.038847969  1.000000000
## DAYS_EMPLOYED                             0.002191339 -0.508824142
## OWN_CAR_AGE                              -0.081436107  0.001110481
## CNT_FAM_MEMBERS                          -0.034209956  0.197954108
## AMR_REQ_CREDIT_BUREAU_SUM                 0.025584835 -0.032038595
##                            DAYS_EMPLOYED  OWN_CAR_AGE CNT_FAM_MEMBERS
## TARGET                      -0.028323415  0.037232346    -0.001250596
## AMT_INCOME_TOTAL            -0.103847372 -0.132171880    -0.001726251
## CNT_CHILDREN                -0.183114593  0.008950126     0.914413945
## AMT_CREDIT                  -0.019371002 -0.092800207     0.020202897
## AMT_ANNUITY                 -0.052459368 -0.096197763     0.030465151
## AMT_GOODS_PRICE             -0.017943510 -0.101852976     0.017797807
## REGION_POPULATION_RELATIVE   0.002191339 -0.081436107    -0.034209956
## DAYS_BIRTH                  -0.508824142  0.001110481     0.197954108
## DAYS_EMPLOYED                1.000000000  0.031961957    -0.152888423
## OWN_CAR_AGE                  0.031961957  1.000000000    -0.014499077
## CNT_FAM_MEMBERS             -0.152888423 -0.014499077     1.000000000
## AMR_REQ_CREDIT_BUREAU_SUM    0.007042087 -0.026658773    -0.018385506
##                            AMR_REQ_CREDIT_BUREAU_SUM
## TARGET                                   0.019143211
## AMT_INCOME_TOTAL                         0.035277693
## CNT_CHILDREN                            -0.029676739
## AMT_CREDIT                              -0.017312145
## AMT_ANNUITY                              0.011445101
## AMT_GOODS_PRICE                         -0.018399870
## REGION_POPULATION_RELATIVE               0.025584835
## DAYS_BIRTH                              -0.032038595
## DAYS_EMPLOYED                            0.007042087
## OWN_CAR_AGE                             -0.026658773
## CNT_FAM_MEMBERS                         -0.018385506
## AMR_REQ_CREDIT_BUREAU_SUM                1.000000000

Based on the correlation table above, two pairs of independent variables have an extremely high correlation: (1) (AMT_CREDIT, AMT_GOODS_PRICE), corr = 0.987 AMT_GOODS_PRICE represents the goods price of good that client asked for on the previous application. AMT_CREDIT represents the final credit amount on the previous application. Through the definitions of these two variables, it’s clear that we could drop one of the variables.

  1. (CNT_FAM_MEMBERS, CNT_CHILDREN), corr = 0.914 CNT_FAM_mEMBERS represents how many family members clients have CNT_CHILDREN represents how many children clients have Through the definitions of these two variables, it’s clear that we should drop one of the variables.
application_data_selected <- application_data_selected %>%
    dplyr::select(-(AMT_GOODS_PRICE)) %>%
    dplyr::select(-(CNT_CHILDREN))

Here we should factorize CNT_FAM_MEMBERS, representing how many family members the loan applicant has, and AMT_REQ_CREDIT_BUREAU_SUM, representing number of enquiries to Credit Bureau about the client one year before submitting his loan application, because both variables are more like categorical rather than continuous, based on their definitions.

#factorize CNT_FAM_MEMBERS, AMR_REQ_CREDIT_BUREAU_SUM
application_data_selected$CNT_FAM_MEMBERS <- as.factor(application_data_selected$CNT_FAM_MEMBERS)
application_data_selected$AMR_REQ_CREDIT_BUREAU_SUM <- as.factor(application_data_selected$AMR_REQ_CREDIT_BUREAU_SUM)

As the dataset is extremely large and my computer’s computing power doesn’t support this size of computations. Therefore, I decided to randomly select 10000 observations to run the following analysis. Besides, the random selection process should be able to represent the population.

set.seed(100)
application_data_sam <- application_data_selected[sample(nrow(application_data_selected), 10000), ]
summary(application_data_sam)
##      TARGET       CODE_GENDER       NAME_CONTRACT_TYPE AMT_INCOME_TOTAL 
##  Min.   :0.0000   F  :6556    Cash loans     :9025     Min.   :  25650  
##  1st Qu.:0.0000   M  :3444    Revolving loans: 975     1st Qu.: 112500  
##  Median :0.0000   XNA:   0                             Median : 148500  
##  Mean   :0.0829                                        Mean   : 167765  
##  3rd Qu.:0.0000                                        3rd Qu.: 202500  
##  Max.   :1.0000                                        Max.   :2250000  
##                                                                         
##  FLAG_OWN_CAR FLAG_OWN_REALTY   AMT_CREDIT       AMT_ANNUITY    
##  N:6540       N:3070          Min.   :  45000   Min.   :  2844  
##  Y:3460       Y:6930          1st Qu.: 270000   1st Qu.: 16574  
##                               Median : 521280   Median : 25047  
##                               Mean   : 599379   Mean   : 27049  
##                               3rd Qu.: 808650   3rd Qu.: 34533  
##                               Max.   :2961000   Max.   :133848  
##                                                                 
##         NAME_TYPE_SUITE             NAME_INCOME_TYPE
##  Unaccompanied  :8115   Working             :5123   
##  Family         :1278   Commercial associate:2335   
##  Spouse, partner: 374   Pensioner           :1804   
##  Children       : 118   State servant       : 735   
##  Other_B        :  50   Businessman         :   1   
##                 :  39   Student             :   1   
##  (Other)        :  26   (Other)             :   1   
##                     NAME_EDUCATION_TYPE            NAME_FAMILY_STATUS
##  Academic degree              :   5     Civil marriage      :1036    
##  Higher education             :2414     Married             :6320    
##  Incomplete higher            : 305     Separated           : 663    
##  Lower secondary              : 114     Single / not married:1469    
##  Secondary / secondary special:7162     Unknown             :   0    
##                                         Widow               : 512    
##                                                                      
##            NAME_HOUSING_TYPE REGION_POPULATION_RELATIVE   DAYS_BIRTH    
##  Co-op apartment    :  45    Min.   :0.000938           Min.   :-25186  
##  House / apartment  :8863    1st Qu.:0.010006           1st Qu.:-19688  
##  Municipal apartment: 356    Median :0.018850           Median :-15820  
##  Office apartment   :  80    Mean   :0.020710           Mean   :-16047  
##  Rented apartment   : 179    3rd Qu.:0.028663           3rd Qu.:-12469  
##  With parents       : 477    Max.   :0.072508           Max.   : -7721  
##                                                                         
##  DAYS_EMPLOYED       OWN_CAR_AGE       OCCUPATION_TYPE CNT_FAM_MEMBERS
##  Min.   :-17546.0   Min.   : 0.00              :3162   2      :5197   
##  1st Qu.: -2729.0   1st Qu.: 5.00   Laborers   :1793   1      :2186   
##  Median : -1181.5   Median : 9.00   Sales staff:1041   3      :1686   
##  Mean   : 63952.6   Mean   :11.97   Core staff : 878   4      : 814   
##  3rd Qu.:  -283.5   3rd Qu.:15.00   Managers   : 667   5      : 101   
##  Max.   :365243.0   Max.   :65.00   Drivers    : 623   6      :  11   
##                     NA's   :6540    (Other)    :1836   (Other):   5   
##               ORGANIZATION_TYPE AMR_REQ_CREDIT_BUREAU_SUM
##  Business Entity Type 3:2229    1      :1719             
##  XNA                   :1804    2      :1696             
##  Self-employed         :1263    0      :1672             
##  Other                 : 529    3      :1304             
##  Business Entity Type 2: 354    4      : 906             
##  Government            : 350    (Other):1376             
##  (Other)               :3471    NA's   :1327

Data Description

Through the two tables below, we can see that the distributions of each variable in the original and new datasets are fairly similar. Besides, we also have some missing values and should consider missing value imputations.

summary(application_data_selected) #original datasets with selected variables
##      TARGET        CODE_GENDER        NAME_CONTRACT_TYPE
##  Min.   :0.00000   F  :202448   Cash loans     :278232  
##  1st Qu.:0.00000   M  :105059   Revolving loans: 29279  
##  Median :0.00000   XNA:     4                           
##  Mean   :0.08073                                        
##  3rd Qu.:0.00000                                        
##  Max.   :1.00000                                        
##                                                         
##  AMT_INCOME_TOTAL    FLAG_OWN_CAR FLAG_OWN_REALTY   AMT_CREDIT     
##  Min.   :    25650   N:202924     N: 94199        Min.   :  45000  
##  1st Qu.:   112500   Y:104587     Y:213312        1st Qu.: 270000  
##  Median :   147150                                Median : 513531  
##  Mean   :   168798                                Mean   : 599026  
##  3rd Qu.:   202500                                3rd Qu.: 808650  
##  Max.   :117000000                                Max.   :4050000  
##                                                                    
##   AMT_ANNUITY            NAME_TYPE_SUITE               NAME_INCOME_TYPE 
##  Min.   :  1616   Unaccompanied  :248526   Working             :158774  
##  1st Qu.: 16524   Family         : 40149   Commercial associate: 71617  
##  Median : 24903   Spouse, partner: 11370   Pensioner           : 55362  
##  Mean   : 27109   Children       :  3267   State servant       : 21703  
##  3rd Qu.: 34596   Other_B        :  1770   Unemployed          :    22  
##  Max.   :258026                  :  1292   Student             :    18  
##  NA's   :12       (Other)        :  1137   (Other)             :    15  
##                     NAME_EDUCATION_TYPE            NAME_FAMILY_STATUS
##  Academic degree              :   164   Civil marriage      : 29775  
##  Higher education             : 74863   Married             :196432  
##  Incomplete higher            : 10277   Separated           : 19770  
##  Lower secondary              :  3816   Single / not married: 45444  
##  Secondary / secondary special:218391   Unknown             :     2  
##                                         Widow               : 16088  
##                                                                      
##            NAME_HOUSING_TYPE  REGION_POPULATION_RELATIVE   DAYS_BIRTH    
##  Co-op apartment    :  1122   Min.   :0.00029            Min.   :-25229  
##  House / apartment  :272868   1st Qu.:0.01001            1st Qu.:-19682  
##  Municipal apartment: 11183   Median :0.01885            Median :-15750  
##  Office apartment   :  2617   Mean   :0.02087            Mean   :-16037  
##  Rented apartment   :  4881   3rd Qu.:0.02866            3rd Qu.:-12413  
##  With parents       : 14840   Max.   :0.07251            Max.   : -7489  
##                                                                          
##  DAYS_EMPLOYED     OWN_CAR_AGE        OCCUPATION_TYPE  CNT_FAM_MEMBERS 
##  Min.   :-17912   Min.   : 0.00               :96391   2      :158357  
##  1st Qu.: -2760   1st Qu.: 5.00    Laborers   :55186   1      : 67847  
##  Median : -1213   Median : 9.00    Sales staff:32102   3      : 52601  
##  Mean   : 63815   Mean   :12.06    Core staff :27570   4      : 24697  
##  3rd Qu.:  -289   3rd Qu.:15.00    Managers   :21371   5      :  3478  
##  Max.   :365243   Max.   :91.00    Drivers    :18603   (Other):   529  
##                   NA's   :202929   (Other)    :56288   NA's   :     2  
##               ORGANIZATION_TYPE  AMR_REQ_CREDIT_BUREAU_SUM
##  Business Entity Type 3: 67992   1      :53914            
##  XNA                   : 55374   2      :51559            
##  Self-employed         : 38412   0      :50911            
##  Other                 : 16683   3      :39380            
##  Medicine              : 11193   4      :27241            
##  Business Entity Type 2: 10553   (Other):42987            
##  (Other)               :107304   NA's   :41519
summary(application_data_sam) #the dataset after the random selection process
##      TARGET       CODE_GENDER       NAME_CONTRACT_TYPE AMT_INCOME_TOTAL 
##  Min.   :0.0000   F  :6556    Cash loans     :9025     Min.   :  25650  
##  1st Qu.:0.0000   M  :3444    Revolving loans: 975     1st Qu.: 112500  
##  Median :0.0000   XNA:   0                             Median : 148500  
##  Mean   :0.0829                                        Mean   : 167765  
##  3rd Qu.:0.0000                                        3rd Qu.: 202500  
##  Max.   :1.0000                                        Max.   :2250000  
##                                                                         
##  FLAG_OWN_CAR FLAG_OWN_REALTY   AMT_CREDIT       AMT_ANNUITY    
##  N:6540       N:3070          Min.   :  45000   Min.   :  2844  
##  Y:3460       Y:6930          1st Qu.: 270000   1st Qu.: 16574  
##                               Median : 521280   Median : 25047  
##                               Mean   : 599379   Mean   : 27049  
##                               3rd Qu.: 808650   3rd Qu.: 34533  
##                               Max.   :2961000   Max.   :133848  
##                                                                 
##         NAME_TYPE_SUITE             NAME_INCOME_TYPE
##  Unaccompanied  :8115   Working             :5123   
##  Family         :1278   Commercial associate:2335   
##  Spouse, partner: 374   Pensioner           :1804   
##  Children       : 118   State servant       : 735   
##  Other_B        :  50   Businessman         :   1   
##                 :  39   Student             :   1   
##  (Other)        :  26   (Other)             :   1   
##                     NAME_EDUCATION_TYPE            NAME_FAMILY_STATUS
##  Academic degree              :   5     Civil marriage      :1036    
##  Higher education             :2414     Married             :6320    
##  Incomplete higher            : 305     Separated           : 663    
##  Lower secondary              : 114     Single / not married:1469    
##  Secondary / secondary special:7162     Unknown             :   0    
##                                         Widow               : 512    
##                                                                      
##            NAME_HOUSING_TYPE REGION_POPULATION_RELATIVE   DAYS_BIRTH    
##  Co-op apartment    :  45    Min.   :0.000938           Min.   :-25186  
##  House / apartment  :8863    1st Qu.:0.010006           1st Qu.:-19688  
##  Municipal apartment: 356    Median :0.018850           Median :-15820  
##  Office apartment   :  80    Mean   :0.020710           Mean   :-16047  
##  Rented apartment   : 179    3rd Qu.:0.028663           3rd Qu.:-12469  
##  With parents       : 477    Max.   :0.072508           Max.   : -7721  
##                                                                         
##  DAYS_EMPLOYED       OWN_CAR_AGE       OCCUPATION_TYPE CNT_FAM_MEMBERS
##  Min.   :-17546.0   Min.   : 0.00              :3162   2      :5197   
##  1st Qu.: -2729.0   1st Qu.: 5.00   Laborers   :1793   1      :2186   
##  Median : -1181.5   Median : 9.00   Sales staff:1041   3      :1686   
##  Mean   : 63952.6   Mean   :11.97   Core staff : 878   4      : 814   
##  3rd Qu.:  -283.5   3rd Qu.:15.00   Managers   : 667   5      : 101   
##  Max.   :365243.0   Max.   :65.00   Drivers    : 623   6      :  11   
##                     NA's   :6540    (Other)    :1836   (Other):   5   
##               ORGANIZATION_TYPE AMR_REQ_CREDIT_BUREAU_SUM
##  Business Entity Type 3:2229    1      :1719             
##  XNA                   :1804    2      :1696             
##  Self-employed         :1263    0      :1672             
##  Other                 : 529    3      :1304             
##  Business Entity Type 2: 354    4      : 906             
##  Government            : 350    (Other):1376             
##  (Other)               :3471    NA's   :1327

Missing values imputation

IWN_CAR_AGE and AMR_REQ_CREDOT_BUREAU_SUM are the two columns that contain missing values in the dataset. I used the mice package to conduct missing value imputation to generate complete datasets. For the imputation method, I chose cart, instead of the default method. I have tried to use the default method to impute missing values; however, it returned the following error “system is computationally singular”. The cause of the problem here could probably be the large number of unbalanced factor variables in the dataset. When these are turned intodummy variables there’s a high probability that one colum is a linear combination of another. As the default imputation methods involve linear regression, this results in a X matrix that cannot be inverted. Therefore, we consider to change the imputation method that is not stochastic, which require no X matrix inversion. (Reference: links)

#check pattern
md.pattern(application_data_sam)

##      TARGET CODE_GENDER NAME_CONTRACT_TYPE AMT_INCOME_TOTAL FLAG_OWN_CAR
## 3069      1           1                  1                1            1
## 5604      1           1                  1                1            1
## 391       1           1                  1                1            1
## 936       1           1                  1                1            1
##           0           0                  0                0            0
##      FLAG_OWN_REALTY AMT_CREDIT AMT_ANNUITY NAME_TYPE_SUITE
## 3069               1          1           1               1
## 5604               1          1           1               1
## 391                1          1           1               1
## 936                1          1           1               1
##                    0          0           0               0
##      NAME_INCOME_TYPE NAME_EDUCATION_TYPE NAME_FAMILY_STATUS
## 3069                1                   1                  1
## 5604                1                   1                  1
## 391                 1                   1                  1
## 936                 1                   1                  1
##                     0                   0                  0
##      NAME_HOUSING_TYPE REGION_POPULATION_RELATIVE DAYS_BIRTH DAYS_EMPLOYED
## 3069                 1                          1          1             1
## 5604                 1                          1          1             1
## 391                  1                          1          1             1
## 936                  1                          1          1             1
##                      0                          0          0             0
##      OCCUPATION_TYPE CNT_FAM_MEMBERS ORGANIZATION_TYPE
## 3069               1               1                 1
## 5604               1               1                 1
## 391                1               1                 1
## 936                1               1                 1
##                    0               0                 0
##      AMR_REQ_CREDIT_BUREAU_SUM OWN_CAR_AGE     
## 3069                         1           1    0
## 5604                         1           0    1
## 391                          0           1    1
## 936                          0           0    2
##                           1327        6540 7867
application_MI <- mice(application_data_sam, m = 10, method = "cart", seed = 8)
## Warning: Number of logged events: 100

Imputation Model Diagnostics

Based on the charts below, we are comfortable with our missing value imputations.

stripplot(application_MI, col=c("grey",mdc(2)),pch=c(1,20))

stripplot(application_MI, OWN_CAR_AGE~TARGET, col=c("grey",mdc(2)),pch=c(1,20), xlab = 'TARGET', ylab = "OWN_CAR_AGE")

stripplot(application_MI, AMR_REQ_CREDIT_BUREAU_SUM~TARGET, col=c("grey",mdc(2)),pch=c(1,20), xlab = 'TARGET', ylab = "AMT_REQ_CREDIT_BUREAU_DAY")

Posterior Predictive Check on two complete datasets

Both the histogram and boxplots look similiar for replica and complete datasets; therefore, we are confident about the quality of the imputation model.

application_ppcheck <- rbind(application_data_sam, application_data_sam)
application_ppcheck[10001:20000, apply(is.na(application_data_sam), any, MARGIN = 2)] <- NA
application_ppcheck_MI <- mice(application_ppcheck, m = 10, method = "cart", seed = 8)
## Warning: Number of logged events: 100
d1ppcheck <- mice::complete(application_ppcheck_MI, 1)
d2ppcheck <- mice::complete(application_ppcheck_MI, 2)
#dataset1
par(mfrow = c(1,2))
boxplot(d1ppcheck$OWN_CAR_AGE[1:10000]~d1ppcheck$TARGET[1:10000], ylab="OWN_CAR_AGE", xlab="TARGET", main = "OWN_CAR_AGE vs TARGET completed data")
boxplot(d1ppcheck$OWN_CAR_AGE[10001:20000]~d1ppcheck$TARGET[10001:20000], ylab="OWN_CAR_AGE", xlab="TARGET", main = "OWN_CAR_AGE vs TARGET completed data")

#Should I treat it as continuous?
par(mfrow = c(2,1))
hist(as.numeric(d1ppcheck$AMR_REQ_CREDIT_BUREAU_SUM[1:10000]), xlab="AMR_REQ_CREDIT_BUREAU_SUM", main = "AMR_REQ_CREDIT_BUREAU_SUM complete data")
hist(as.numeric(d1ppcheck$AMR_REQ_CREDIT_BUREAU_SUM[10001:20000]), xlab="AMR_REQ_CREDIT_BUREAU_SUM", main = "AMR_REQ_CREDIT_BUREAU_SUM replicated data")

Regression Model

reg <- with(data = application_MI, glm(TARGET ~ CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY
            + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE 
            + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS 
            + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM),
            family = binomial)
summary(pool(reg))
##                                                       estimate
## (Intercept)                                       2.063259e-01
## CODE_GENDERM                                      4.352771e-02
## NAME_CONTRACT_TYPERevolving loans                -5.055284e-02
## AMT_INCOME_TOTAL                                 -5.315301e-08
## FLAG_OWN_CARY                                    -3.081047e-02
## FLAG_OWN_REALTYY                                  5.206398e-03
## AMT_CREDIT                                       -1.320492e-08
## AMT_ANNUITY                                      -1.769943e-07
## NAME_TYPE_SUITEChildren                          -2.756176e-02
## NAME_TYPE_SUITEFamily                            -9.752861e-03
## NAME_TYPE_SUITEGroup of people                   -1.140243e-01
## NAME_TYPE_SUITEOther_A                           -6.362593e-02
## NAME_TYPE_SUITEOther_B                           -6.131117e-02
## NAME_TYPE_SUITESpouse, partner                   -9.428628e-03
## NAME_TYPE_SUITEUnaccompanied                     -1.514317e-02
## NAME_INCOME_TYPECommercial associate             -1.027763e-01
## NAME_INCOME_TYPEPensioner                        -1.580174e-01
## NAME_INCOME_TYPEState servant                    -1.063998e-01
## NAME_INCOME_TYPEStudent                          -9.719079e-02
## NAME_INCOME_TYPEUnemployed                       -2.097608e-01
## NAME_INCOME_TYPEWorking                          -8.310605e-02
## NAME_EDUCATION_TYPEHigher education               3.705344e-02
## NAME_EDUCATION_TYPEIncomplete higher              5.441029e-02
## NAME_EDUCATION_TYPELower secondary                7.767959e-02
## NAME_EDUCATION_TYPESecondary / secondary special  5.559630e-02
## NAME_FAMILY_STATUSMarried                        -6.035944e-03
## NAME_FAMILY_STATUSSeparated                      -1.670707e-03
## NAME_FAMILY_STATUSSingle / not married            7.999912e-03
## NAME_FAMILY_STATUSWidow                           1.727822e-02
## NAME_HOUSING_TYPEHouse / apartment                5.891942e-03
## NAME_HOUSING_TYPEMunicipal apartment             -2.051035e-03
## NAME_HOUSING_TYPEOffice apartment                -4.512994e-02
## NAME_HOUSING_TYPERented apartment                 3.482144e-02
## NAME_HOUSING_TYPEWith parents                     2.526804e-02
## REGION_POPULATION_RELATIVE                       -9.805655e-02
## DAYS_BIRTH                                        3.216099e-06
## DAYS_EMPLOYED                                     6.241027e-06
## OWN_CAR_AGE                                       1.037233e-04
## OCCUPATION_TYPEAccountants                       -1.463772e-02
## OCCUPATION_TYPECleaning staff                    -1.694824e-02
## OCCUPATION_TYPECooking staff                      1.418259e-02
## OCCUPATION_TYPECore staff                        -5.402408e-03
## OCCUPATION_TYPEDrivers                            1.028013e-02
## OCCUPATION_TYPEHigh skill tech staff             -1.774165e-02
## OCCUPATION_TYPEHR staff                          -7.604784e-03
## OCCUPATION_TYPEIT staff                          -2.806577e-02
## OCCUPATION_TYPELaborers                          -4.816502e-03
## OCCUPATION_TYPELow-skill Laborers                 2.681807e-02
## OCCUPATION_TYPEManagers                          -5.676823e-03
## OCCUPATION_TYPEMedicine staff                    -2.491503e-02
## OCCUPATION_TYPEPrivate service staff             -6.379935e-03
## OCCUPATION_TYPERealty agents                      7.190980e-02
## OCCUPATION_TYPESales staff                       -4.065929e-03
## OCCUPATION_TYPESecretaries                        4.072782e-02
## OCCUPATION_TYPESecurity staff                     4.084883e-02
## OCCUPATION_TYPEWaiters/barmen staff               1.002301e-01
## CNT_FAM_MEMBERS2                                  1.132116e-03
## CNT_FAM_MEMBERS3                                 -2.320473e-03
## CNT_FAM_MEMBERS4                                 -5.599006e-03
## CNT_FAM_MEMBERS5                                  3.401739e-02
## CNT_FAM_MEMBERS6                                  1.794479e-01
## CNT_FAM_MEMBERS7                                  8.906367e-02
## ORGANIZATION_TYPEAgriculture                      4.382414e-03
## ORGANIZATION_TYPEBank                             8.570364e-03
## ORGANIZATION_TYPEBusiness Entity Type 1          -1.214637e-03
## ORGANIZATION_TYPEBusiness Entity Type 2           3.669097e-02
## ORGANIZATION_TYPEBusiness Entity Type 3           8.953085e-03
## ORGANIZATION_TYPECleaning                         3.671069e-02
## ORGANIZATION_TYPEConstruction                     2.132647e-02
## ORGANIZATION_TYPECulture                         -5.861336e-02
## ORGANIZATION_TYPEElectricity                     -1.564123e-02
## ORGANIZATION_TYPEEmergency                       -2.077316e-02
## ORGANIZATION_TYPEGovernment                       1.162231e-03
## ORGANIZATION_TYPEHotel                           -6.344843e-02
## ORGANIZATION_TYPEHousing                          5.454882e-03
## ORGANIZATION_TYPEIndustry: type 1                -3.756790e-02
## ORGANIZATION_TYPEIndustry: type 11                1.558636e-02
## ORGANIZATION_TYPEIndustry: type 12                9.957355e-02
## ORGANIZATION_TYPEIndustry: type 13               -1.205705e-01
## ORGANIZATION_TYPEIndustry: type 2                -7.879773e-02
## ORGANIZATION_TYPEIndustry: type 3                 4.168363e-02
## ORGANIZATION_TYPEIndustry: type 4                 1.304441e-02
## ORGANIZATION_TYPEIndustry: type 5                 1.303146e-02
## ORGANIZATION_TYPEIndustry: type 6                -7.803666e-02
## ORGANIZATION_TYPEIndustry: type 7                 2.541053e-02
## ORGANIZATION_TYPEIndustry: type 9                 3.122153e-02
## ORGANIZATION_TYPEInsurance                       -7.338810e-02
## ORGANIZATION_TYPEKindergarten                    -3.629599e-03
## ORGANIZATION_TYPELegal Services                   1.982034e-02
## ORGANIZATION_TYPEMedicine                         3.709787e-02
## ORGANIZATION_TYPEMilitary                        -3.655942e-02
## ORGANIZATION_TYPEMobile                          -9.156294e-02
## ORGANIZATION_TYPEOther                            1.135235e-03
## ORGANIZATION_TYPEPolice                          -3.770840e-02
## ORGANIZATION_TYPEPostal                          -6.280312e-03
## ORGANIZATION_TYPERealtor                          2.169619e-01
## ORGANIZATION_TYPEReligion                        -3.296117e-02
## ORGANIZATION_TYPERestaurant                      -1.767501e-02
## ORGANIZATION_TYPESchool                           8.833369e-04
## ORGANIZATION_TYPESecurity                        -3.087008e-02
## ORGANIZATION_TYPESecurity Ministries             -3.000705e-03
## ORGANIZATION_TYPESelf-employed                    1.969723e-02
## ORGANIZATION_TYPEServices                        -1.091431e-02
## ORGANIZATION_TYPETelecom                         -2.065610e-02
## ORGANIZATION_TYPETrade: type 1                    1.810593e-02
## ORGANIZATION_TYPETrade: type 2                   -1.085085e-03
## ORGANIZATION_TYPETrade: type 3                   -1.529725e-02
## ORGANIZATION_TYPETrade: type 4                   -1.012080e-01
## ORGANIZATION_TYPETrade: type 6                   -9.123168e-03
## ORGANIZATION_TYPETrade: type 7                    3.726542e-02
## ORGANIZATION_TYPETransport: type 1               -7.505025e-02
## ORGANIZATION_TYPETransport: type 2                7.819173e-02
## ORGANIZATION_TYPETransport: type 3                1.384465e-01
## ORGANIZATION_TYPETransport: type 4               -3.459733e-02
## ORGANIZATION_TYPEUniversity                      -3.775120e-04
## ORGANIZATION_TYPEXNA                             -2.240731e+00
## AMR_REQ_CREDIT_BUREAU_SUM1                        4.190379e-03
## AMR_REQ_CREDIT_BUREAU_SUM2                        1.092473e-02
## AMR_REQ_CREDIT_BUREAU_SUM3                        1.288864e-02
## AMR_REQ_CREDIT_BUREAU_SUM4                        3.119740e-03
## AMR_REQ_CREDIT_BUREAU_SUM5                       -7.012164e-03
## AMR_REQ_CREDIT_BUREAU_SUM6                        2.503563e-02
## AMR_REQ_CREDIT_BUREAU_SUM7                        1.588082e-02
## AMR_REQ_CREDIT_BUREAU_SUM8                        5.843887e-02
## AMR_REQ_CREDIT_BUREAU_SUM9                        1.691654e-02
## AMR_REQ_CREDIT_BUREAU_SUM10                      -1.849285e-02
## AMR_REQ_CREDIT_BUREAU_SUM11                       3.053713e-03
## AMR_REQ_CREDIT_BUREAU_SUM12                      -4.140642e-02
## AMR_REQ_CREDIT_BUREAU_SUM13                      -7.344946e-02
## AMR_REQ_CREDIT_BUREAU_SUM14                      -1.746657e-02
## AMR_REQ_CREDIT_BUREAU_SUM15                      -8.302970e-02
## AMR_REQ_CREDIT_BUREAU_SUM16                      -2.768300e-02
## AMR_REQ_CREDIT_BUREAU_SUM17                      -9.937823e-02
## AMR_REQ_CREDIT_BUREAU_SUM18                      -7.341512e-02
## AMR_REQ_CREDIT_BUREAU_SUM19                      -1.680945e-02
## AMR_REQ_CREDIT_BUREAU_SUM20                      -5.056997e-02
## AMR_REQ_CREDIT_BUREAU_SUM28                      -1.117664e-01
##                                                     std.error    statistic
## (Intercept)                                      3.238824e-01  0.637039695
## CODE_GENDERM                                     7.349921e-03  5.922200671
## NAME_CONTRACT_TYPERevolving loans                9.804538e-03 -5.156065232
## AMT_INCOME_TOTAL                                 3.806233e-08 -1.396472747
## FLAG_OWN_CARY                                    6.335356e-03 -4.863258282
## FLAG_OWN_REALTYY                                 6.208820e-03  0.838548695
## AMT_CREDIT                                       1.107826e-08 -1.191966886
## AMT_ANNUITY                                      3.253000e-07 -0.544095497
## NAME_TYPE_SUITEChildren                          5.078004e-02 -0.542767609
## NAME_TYPE_SUITEFamily                            4.468996e-02 -0.218233848
## NAME_TYPE_SUITEGroup of people                   1.298177e-01 -0.878341280
## NAME_TYPE_SUITEOther_A                           7.422377e-02 -0.857217725
## NAME_TYPE_SUITEOther_B                           5.868265e-02 -1.044792141
## NAME_TYPE_SUITESpouse, partner                   4.628181e-02 -0.203722120
## NAME_TYPE_SUITEUnaccompanied                     4.412372e-02 -0.343197977
## NAME_INCOME_TYPECommercial associate             2.844942e-01 -0.361259798
## NAME_INCOME_TYPEPensioner                        3.946596e-01 -0.400389083
## NAME_INCOME_TYPEState servant                    2.848237e-01 -0.373563697
## NAME_INCOME_TYPEStudent                          3.901416e-01 -0.249116717
## NAME_INCOME_TYPEUnemployed                       4.799008e-01 -0.437092064
## NAME_INCOME_TYPEWorking                          2.846590e-01 -0.291949486
## NAME_EDUCATION_TYPEHigher education              1.222726e-01  0.303039512
## NAME_EDUCATION_TYPEIncomplete higher             1.231872e-01  0.441687961
## NAME_EDUCATION_TYPELower secondary               1.248090e-01  0.622387764
## NAME_EDUCATION_TYPESecondary / secondary special 1.221992e-01  0.454964697
## NAME_FAMILY_STATUSMarried                        9.324405e-03 -0.647327578
## NAME_FAMILY_STATUSSeparated                      1.701766e-02 -0.098174855
## NAME_FAMILY_STATUSSingle / not married           1.680871e-02  0.475938598
## NAME_FAMILY_STATUSWidow                          2.034232e-02  0.849372761
## NAME_HOUSING_TYPEHouse / apartment               4.101925e-02  0.143638448
## NAME_HOUSING_TYPEMunicipal apartment             4.344237e-02 -0.047212774
## NAME_HOUSING_TYPEOffice apartment                5.133026e-02 -0.879207432
## NAME_HOUSING_TYPERented apartment                4.573693e-02  0.761341852
## NAME_HOUSING_TYPEWith parents                    4.277699e-02  0.590692396
## REGION_POPULATION_RELATIVE                       2.097866e-01 -0.467410822
## DAYS_BIRTH                                       9.715712e-07  3.310204273
## DAYS_EMPLOYED                                    1.476965e-06  4.225575963
## OWN_CAR_AGE                                      2.801423e-04  0.370252048
## OCCUPATION_TYPEAccountants                       1.778898e-02 -0.822853488
## OCCUPATION_TYPECleaning staff                    2.441296e-02 -0.694231084
## OCCUPATION_TYPECooking staff                     2.101915e-02  0.674746192
## OCCUPATION_TYPECore staff                        1.324128e-02 -0.407997419
## OCCUPATION_TYPEDrivers                           1.444968e-02  0.711443845
## OCCUPATION_TYPEHigh skill tech staff             1.559747e-02 -1.137469734
## OCCUPATION_TYPEHR staff                          7.360465e-02 -0.103319349
## OCCUPATION_TYPEIT staff                          6.037217e-02 -0.464879335
## OCCUPATION_TYPELaborers                          1.040000e-02 -0.463125263
## OCCUPATION_TYPELow-skill Laborers                3.512250e-02  0.763558124
## OCCUPATION_TYPEManagers                          1.342852e-02 -0.422743750
## OCCUPATION_TYPEMedicine staff                    2.288624e-02 -1.088646858
## OCCUPATION_TYPEPrivate service staff             3.046778e-02 -0.209399389
## OCCUPATION_TYPERealty agents                     5.679346e-02  1.266163399
## OCCUPATION_TYPESales staff                       1.227742e-02 -0.331171413
## OCCUPATION_TYPESecretaries                       4.763194e-02  0.855052630
## OCCUPATION_TYPESecurity staff                    2.237480e-02  1.825662323
## OCCUPATION_TYPEWaiters/barmen staff              4.716424e-02  2.125129651
## CNT_FAM_MEMBERS2                                 1.464121e-02  0.077323907
## CNT_FAM_MEMBERS3                                 1.639724e-02 -0.141516061
## CNT_FAM_MEMBERS4                                 1.817850e-02 -0.308001501
## CNT_FAM_MEMBERS5                                 3.137761e-02  1.084129310
## CNT_FAM_MEMBERS6                                 8.416156e-02  2.132183844
## CNT_FAM_MEMBERS7                                 1.237411e-01  0.719758220
## ORGANIZATION_TYPEAgriculture                     7.110094e-02  0.061636519
## ORGANIZATION_TYPEBank                            7.345444e-02  0.116675911
## ORGANIZATION_TYPEBusiness Entity Type 1          6.733533e-02 -0.018038620
## ORGANIZATION_TYPEBusiness Entity Type 2          6.608540e-02  0.555205375
## ORGANIZATION_TYPEBusiness Entity Type 3          6.467652e-02  0.138428667
## ORGANIZATION_TYPECleaning                        1.114015e-01  0.329534866
## ORGANIZATION_TYPEConstruction                    6.707111e-02  0.317968028
## ORGANIZATION_TYPECulture                         9.999669e-02 -0.586153024
## ORGANIZATION_TYPEElectricity                     8.055834e-02 -0.194160226
## ORGANIZATION_TYPEEmergency                       8.792009e-02 -0.236273141
## ORGANIZATION_TYPEGovernment                      6.604658e-02  0.017597149
## ORGANIZATION_TYPEHotel                           8.257608e-02 -0.768363320
## ORGANIZATION_TYPEHousing                         6.982835e-02  0.078118440
## ORGANIZATION_TYPEIndustry: type 1                7.841714e-02 -0.479077683
## ORGANIZATION_TYPEIndustry: type 11               7.098229e-02  0.219580928
## ORGANIZATION_TYPEIndustry: type 12               1.020855e-01  0.975393521
## ORGANIZATION_TYPEIndustry: type 13               2.036842e-01 -0.591948495
## ORGANIZATION_TYPEIndustry: type 2                9.731529e-02 -0.809715791
## ORGANIZATION_TYPEIndustry: type 3                6.914345e-02  0.602857174
## ORGANIZATION_TYPEIndustry: type 4                7.817763e-02  0.166856096
## ORGANIZATION_TYPEIndustry: type 5                8.878250e-02  0.146779649
## ORGANIZATION_TYPEIndustry: type 6                1.380145e-01 -0.565423688
## ORGANIZATION_TYPEIndustry: type 7                7.682659e-02  0.330751780
## ORGANIZATION_TYPEIndustry: type 9                6.945843e-02  0.449499499
## ORGANIZATION_TYPEInsurance                       9.119419e-02 -0.804745374
## ORGANIZATION_TYPEKindergarten                    6.709661e-02 -0.054095110
## ORGANIZATION_TYPELegal Services                  9.761973e-02  0.203036166
## ORGANIZATION_TYPEMedicine                        6.714712e-02  0.552486377
## ORGANIZATION_TYPEMilitary                        7.111523e-02 -0.514086997
## ORGANIZATION_TYPEMobile                          1.046480e-01 -0.874961544
## ORGANIZATION_TYPEOther                           6.550997e-02  0.017329199
## ORGANIZATION_TYPEPolice                          7.237334e-02 -0.521026129
## ORGANIZATION_TYPEPostal                          7.224699e-02 -0.086928360
## ORGANIZATION_TYPERealtor                         1.045732e-01  2.074736910
## ORGANIZATION_TYPEReligion                        2.033270e-01 -0.162109178
## ORGANIZATION_TYPERestaurant                      7.372632e-02 -0.239738171
## ORGANIZATION_TYPESchool                          6.659657e-02  0.013263999
## ORGANIZATION_TYPESecurity                        7.165784e-02 -0.430798349
## ORGANIZATION_TYPESecurity Ministries             7.303849e-02 -0.041083890
## ORGANIZATION_TYPESelf-employed                   6.488590e-02  0.303567190
## ORGANIZATION_TYPEServices                        7.689387e-02 -0.141939911
## ORGANIZATION_TYPETelecom                         8.996517e-02 -0.229601124
## ORGANIZATION_TYPETrade: type 1                   1.045225e-01  0.173225100
## ORGANIZATION_TYPETrade: type 2                   7.420166e-02 -0.014623457
## ORGANIZATION_TYPETrade: type 3                   6.905253e-02 -0.221530686
## ORGANIZATION_TYPETrade: type 4                   2.036165e-01 -0.497052183
## ORGANIZATION_TYPETrade: type 6                   8.610919e-02 -0.105948836
## ORGANIZATION_TYPETrade: type 7                   6.674541e-02  0.558321819
## ORGANIZATION_TYPETransport: type 1               1.510872e-01 -0.496734785
## ORGANIZATION_TYPETransport: type 2               7.187849e-02  1.087832087
## ORGANIZATION_TYPETransport: type 3               7.668451e-02  1.805403139
## ORGANIZATION_TYPETransport: type 4               6.837164e-02 -0.506018664
## ORGANIZATION_TYPEUniversity                      7.928623e-02 -0.004761382
## ORGANIZATION_TYPEXNA                             6.089517e-01 -3.679653516
## AMR_REQ_CREDIT_BUREAU_SUM1                       9.127409e-03  0.459098434
## AMR_REQ_CREDIT_BUREAU_SUM2                       9.816924e-03  1.112846530
## AMR_REQ_CREDIT_BUREAU_SUM3                       1.053364e-02  1.223569960
## AMR_REQ_CREDIT_BUREAU_SUM4                       1.161135e-02  0.268680246
## AMR_REQ_CREDIT_BUREAU_SUM5                       1.360600e-02 -0.515373121
## AMR_REQ_CREDIT_BUREAU_SUM6                       1.725497e-02  1.450922280
## AMR_REQ_CREDIT_BUREAU_SUM7                       1.898283e-02  0.836588629
## AMR_REQ_CREDIT_BUREAU_SUM8                       2.739782e-02  2.132974918
## AMR_REQ_CREDIT_BUREAU_SUM9                       3.608539e-02  0.468792048
## AMR_REQ_CREDIT_BUREAU_SUM10                      4.468145e-02 -0.413882025
## AMR_REQ_CREDIT_BUREAU_SUM11                      7.004005e-02  0.043599527
## AMR_REQ_CREDIT_BUREAU_SUM12                      1.057255e-01 -0.391640785
## AMR_REQ_CREDIT_BUREAU_SUM13                      1.157125e-01 -0.634758237
## AMR_REQ_CREDIT_BUREAU_SUM14                      1.799169e-01 -0.097081323
## AMR_REQ_CREDIT_BUREAU_SUM15                      1.321943e-01 -0.628088210
## AMR_REQ_CREDIT_BUREAU_SUM16                      2.742893e-01 -0.100926295
## AMR_REQ_CREDIT_BUREAU_SUM17                      2.726974e-01 -0.364426748
## AMR_REQ_CREDIT_BUREAU_SUM18                      1.840078e-01 -0.398978291
## AMR_REQ_CREDIT_BUREAU_SUM19                      2.746293e-01 -0.061207775
## AMR_REQ_CREDIT_BUREAU_SUM20                      2.562267e-01 -0.197364150
## AMR_REQ_CREDIT_BUREAU_SUM28                      2.599306e-01 -0.429985680
##                                                          df      p.value
## (Intercept)                                      9842.13067 5.241138e-01
## CODE_GENDERM                                     9847.07017 3.282767e-09
## NAME_CONTRACT_TYPERevolving loans                9667.51517 2.570858e-07
## AMT_INCOME_TOTAL                                 9030.00715 1.626036e-01
## FLAG_OWN_CARY                                    9859.54073 1.172539e-06
## FLAG_OWN_REALTYY                                 9757.98866 4.017429e-01
## AMT_CREDIT                                       9807.46426 2.333029e-01
## AMT_ANNUITY                                      9841.48892 5.863881e-01
## NAME_TYPE_SUITEChildren                          9835.74541 5.873021e-01
## NAME_TYPE_SUITEFamily                            9851.82268 8.272514e-01
## NAME_TYPE_SUITEGroup of people                   9854.89304 3.797799e-01
## NAME_TYPE_SUITEOther_A                           9856.74596 3.913454e-01
## NAME_TYPE_SUITEOther_B                           9853.70456 2.961447e-01
## NAME_TYPE_SUITESpouse, partner                   9854.28935 8.385749e-01
## NAME_TYPE_SUITEUnaccompanied                     9846.19867 7.314568e-01
## NAME_INCOME_TYPECommercial associate             9845.81218 7.179130e-01
## NAME_INCOME_TYPEPensioner                        9850.37841 6.888786e-01
## NAME_INCOME_TYPEState servant                    9846.11085 7.087370e-01
## NAME_INCOME_TYPEStudent                          9858.09436 8.032757e-01
## NAME_INCOME_TYPEUnemployed                       9852.56370 6.620542e-01
## NAME_INCOME_TYPEWorking                          9845.49705 7.703314e-01
## NAME_EDUCATION_TYPEHigher education              9857.85728 7.618661e-01
## NAME_EDUCATION_TYPEIncomplete higher             9857.92865 6.587247e-01
## NAME_EDUCATION_TYPELower secondary               9856.82413 5.337013e-01
## NAME_EDUCATION_TYPESecondary / secondary special 9858.02816 6.491447e-01
## NAME_FAMILY_STATUSMarried                        9851.19907 5.174350e-01
## NAME_FAMILY_STATUSSeparated                      9852.71466 9.217954e-01
## NAME_FAMILY_STATUSSingle / not married           9855.64716 6.341287e-01
## NAME_FAMILY_STATUSWidow                          9801.82300 3.956945e-01
## NAME_HOUSING_TYPEHouse / apartment               9850.15576 8.857889e-01
## NAME_HOUSING_TYPEMunicipal apartment             9852.04325 9.623446e-01
## NAME_HOUSING_TYPEOffice apartment                9855.89066 3.793102e-01
## NAME_HOUSING_TYPERented apartment                9857.25008 4.464711e-01
## NAME_HOUSING_TYPEWith parents                    9853.18499 5.547401e-01
## REGION_POPULATION_RELATIVE                       9810.09843 6.402163e-01
## DAYS_BIRTH                                       9852.36184 9.356256e-04
## DAYS_EMPLOYED                                    9851.66253 2.404757e-05
## OWN_CAR_AGE                                        62.44924 7.112027e-01
## OCCUPATION_TYPEAccountants                       9795.14360 4.106112e-01
## OCCUPATION_TYPECleaning staff                    9849.07232 4.875537e-01
## OCCUPATION_TYPECooking staff                     9859.54353 4.998529e-01
## OCCUPATION_TYPECore staff                        9857.26052 6.832844e-01
## OCCUPATION_TYPEDrivers                           9849.62182 4.768261e-01
## OCCUPATION_TYPEHigh skill tech staff             9857.56269 2.553696e-01
## OCCUPATION_TYPEHR staff                          9845.41388 9.177116e-01
## OCCUPATION_TYPEIT staff                          9852.56666 6.420281e-01
## OCCUPATION_TYPELaborers                          9838.74883 6.432848e-01
## OCCUPATION_TYPELow-skill Laborers                9839.07310 4.451489e-01
## OCCUPATION_TYPEManagers                          9854.13464 6.724915e-01
## OCCUPATION_TYPEMedicine staff                    9845.39368 2.763363e-01
## OCCUPATION_TYPEPrivate service staff             9857.24427 8.341408e-01
## OCCUPATION_TYPERealty agents                     9858.84639 2.054845e-01
## OCCUPATION_TYPESales staff                       9859.23310 7.405221e-01
## OCCUPATION_TYPESecretaries                       9855.14485 3.925428e-01
## OCCUPATION_TYPESecurity staff                    9850.20513 6.793137e-02
## OCCUPATION_TYPEWaiters/barmen staff              9857.58809 3.360059e-02
## CNT_FAM_MEMBERS2                                 9857.49802 9.383674e-01
## CNT_FAM_MEMBERS3                                 9858.30430 8.874652e-01
## CNT_FAM_MEMBERS4                                 9859.79692 7.580877e-01
## CNT_FAM_MEMBERS5                                 9859.60681 2.783340e-01
## CNT_FAM_MEMBERS6                                 9859.79036 3.301638e-02
## CNT_FAM_MEMBERS7                                 9859.90649 4.716909e-01
## ORGANIZATION_TYPEAgriculture                     9851.96946 9.508535e-01
## ORGANIZATION_TYPEBank                            9853.09656 9.071193e-01
## ORGANIZATION_TYPEBusiness Entity Type 1          9849.95852 9.856084e-01
## ORGANIZATION_TYPEBusiness Entity Type 2          9852.76155 5.787668e-01
## ORGANIZATION_TYPEBusiness Entity Type 3          9849.04393 8.899045e-01
## ORGANIZATION_TYPECleaning                        9852.78481 7.417584e-01
## ORGANIZATION_TYPEConstruction                    9846.97936 7.505159e-01
## ORGANIZATION_TYPECulture                         9855.28352 5.577861e-01
## ORGANIZATION_TYPEElectricity                     9855.34649 8.460544e-01
## ORGANIZATION_TYPEEmergency                       9854.58245 8.132256e-01
## ORGANIZATION_TYPEGovernment                      9849.30070 9.859606e-01
## ORGANIZATION_TYPEHotel                           9856.82784 4.422898e-01
## ORGANIZATION_TYPEHousing                         9853.55464 9.377354e-01
## ORGANIZATION_TYPEIndustry: type 1                9849.99755 6.318940e-01
## ORGANIZATION_TYPEIndustry: type 11               9848.57262 8.262021e-01
## ORGANIZATION_TYPEIndustry: type 12               9856.01441 3.293890e-01
## ORGANIZATION_TYPEIndustry: type 13               9852.40309 5.538987e-01
## ORGANIZATION_TYPEIndustry: type 2                9856.14096 4.181231e-01
## ORGANIZATION_TYPEIndustry: type 3                9849.73606 5.466176e-01
## ORGANIZATION_TYPEIndustry: type 4                9852.96031 8.674867e-01
## ORGANIZATION_TYPEIndustry: type 5                9855.15334 8.833089e-01
## ORGANIZATION_TYPEIndustry: type 6                9857.90329 5.717985e-01
## ORGANIZATION_TYPEIndustry: type 7                9848.23792 7.408390e-01
## ORGANIZATION_TYPEIndustry: type 9                9851.75824 6.530812e-01
## ORGANIZATION_TYPEInsurance                       9853.96224 4.209860e-01
## ORGANIZATION_TYPEKindergarten                    9850.80883 9.568605e-01
## ORGANIZATION_TYPELegal Services                  9853.80085 8.391110e-01
## ORGANIZATION_TYPEMedicine                        9851.10286 5.806277e-01
## ORGANIZATION_TYPEMilitary                        9853.58736 6.072027e-01
## ORGANIZATION_TYPEMobile                          9857.00555 3.816161e-01
## ORGANIZATION_TYPEOther                           9850.35832 9.861743e-01
## ORGANIZATION_TYPEPolice                          9852.53848 6.023603e-01
## ORGANIZATION_TYPEPostal                          9845.47055 9.307302e-01
## ORGANIZATION_TYPERealtor                         9857.75687 3.803680e-02
## ORGANIZATION_TYPEReligion                        9858.83227 8.712232e-01
## ORGANIZATION_TYPERestaurant                      9849.14060 8.105382e-01
## ORGANIZATION_TYPESchool                          9850.11849 9.894174e-01
## ORGANIZATION_TYPESecurity                        9851.16020 6.666244e-01
## ORGANIZATION_TYPESecurity Ministries             9849.88328 9.672298e-01
## ORGANIZATION_TYPESelf-employed                   9850.71347 7.614641e-01
## ORGANIZATION_TYPEServices                        9855.21952 8.871304e-01
## ORGANIZATION_TYPETelecom                         9855.53402 8.184065e-01
## ORGANIZATION_TYPETrade: type 1                   9847.12069 8.624781e-01
## ORGANIZATION_TYPETrade: type 2                   9850.05884 9.883329e-01
## ORGANIZATION_TYPETrade: type 3                   9842.19071 8.246838e-01
## ORGANIZATION_TYPETrade: type 4                   9858.41253 6.191633e-01
## ORGANIZATION_TYPETrade: type 6                   9850.30181 9.156251e-01
## ORGANIZATION_TYPETrade: type 7                   9848.48774 5.766373e-01
## ORGANIZATION_TYPETransport: type 1               9857.65978 6.193872e-01
## ORGANIZATION_TYPETransport: type 2               9852.87199 2.766958e-01
## ORGANIZATION_TYPETransport: type 3               9854.21526 7.104211e-02
## ORGANIZATION_TYPETransport: type 4               9849.40373 6.128549e-01
## ORGANIZATION_TYPEUniversity                      9851.63858 9.962011e-01
## ORGANIZATION_TYPEXNA                             9855.66774 2.347956e-04
## AMR_REQ_CREDIT_BUREAU_SUM1                       1417.16708 6.461736e-01
## AMR_REQ_CREDIT_BUREAU_SUM2                        250.28318 2.658015e-01
## AMR_REQ_CREDIT_BUREAU_SUM3                        260.84440 2.211437e-01
## AMR_REQ_CREDIT_BUREAU_SUM4                        311.38428 7.881814e-01
## AMR_REQ_CREDIT_BUREAU_SUM5                        499.95796 6.063039e-01
## AMR_REQ_CREDIT_BUREAU_SUM6                        172.24968 1.468333e-01
## AMR_REQ_CREDIT_BUREAU_SUM7                        521.47606 4.028441e-01
## AMR_REQ_CREDIT_BUREAU_SUM8                        302.47679 3.295141e-02
## AMR_REQ_CREDIT_BUREAU_SUM9                        276.96744 6.392286e-01
## AMR_REQ_CREDIT_BUREAU_SUM10                      6861.17223 6.789695e-01
## AMR_REQ_CREDIT_BUREAU_SUM11                      1787.91692 9.652245e-01
## AMR_REQ_CREDIT_BUREAU_SUM12                      9659.66009 6.953321e-01
## AMR_REQ_CREDIT_BUREAU_SUM13                      9710.15537 5.256008e-01
## AMR_REQ_CREDIT_BUREAU_SUM14                      9819.14406 9.226638e-01
## AMR_REQ_CREDIT_BUREAU_SUM15                      9501.72835 5.299607e-01
## AMR_REQ_CREDIT_BUREAU_SUM16                      9859.90649 9.196110e-01
## AMR_REQ_CREDIT_BUREAU_SUM17                      9859.35814 7.155472e-01
## AMR_REQ_CREDIT_BUREAU_SUM18                      9491.65927 6.899178e-01
## AMR_REQ_CREDIT_BUREAU_SUM19                      9859.90649 9.511950e-01
## AMR_REQ_CREDIT_BUREAU_SUM20                      9858.65550 8.435467e-01
## AMR_REQ_CREDIT_BUREAU_SUM28                      9833.27900 6.672155e-01
summary(pool(reg), conf.int=T)
##                                                       estimate
## (Intercept)                                       2.063259e-01
## CODE_GENDERM                                      4.352771e-02
## NAME_CONTRACT_TYPERevolving loans                -5.055284e-02
## AMT_INCOME_TOTAL                                 -5.315301e-08
## FLAG_OWN_CARY                                    -3.081047e-02
## FLAG_OWN_REALTYY                                  5.206398e-03
## AMT_CREDIT                                       -1.320492e-08
## AMT_ANNUITY                                      -1.769943e-07
## NAME_TYPE_SUITEChildren                          -2.756176e-02
## NAME_TYPE_SUITEFamily                            -9.752861e-03
## NAME_TYPE_SUITEGroup of people                   -1.140243e-01
## NAME_TYPE_SUITEOther_A                           -6.362593e-02
## NAME_TYPE_SUITEOther_B                           -6.131117e-02
## NAME_TYPE_SUITESpouse, partner                   -9.428628e-03
## NAME_TYPE_SUITEUnaccompanied                     -1.514317e-02
## NAME_INCOME_TYPECommercial associate             -1.027763e-01
## NAME_INCOME_TYPEPensioner                        -1.580174e-01
## NAME_INCOME_TYPEState servant                    -1.063998e-01
## NAME_INCOME_TYPEStudent                          -9.719079e-02
## NAME_INCOME_TYPEUnemployed                       -2.097608e-01
## NAME_INCOME_TYPEWorking                          -8.310605e-02
## NAME_EDUCATION_TYPEHigher education               3.705344e-02
## NAME_EDUCATION_TYPEIncomplete higher              5.441029e-02
## NAME_EDUCATION_TYPELower secondary                7.767959e-02
## NAME_EDUCATION_TYPESecondary / secondary special  5.559630e-02
## NAME_FAMILY_STATUSMarried                        -6.035944e-03
## NAME_FAMILY_STATUSSeparated                      -1.670707e-03
## NAME_FAMILY_STATUSSingle / not married            7.999912e-03
## NAME_FAMILY_STATUSWidow                           1.727822e-02
## NAME_HOUSING_TYPEHouse / apartment                5.891942e-03
## NAME_HOUSING_TYPEMunicipal apartment             -2.051035e-03
## NAME_HOUSING_TYPEOffice apartment                -4.512994e-02
## NAME_HOUSING_TYPERented apartment                 3.482144e-02
## NAME_HOUSING_TYPEWith parents                     2.526804e-02
## REGION_POPULATION_RELATIVE                       -9.805655e-02
## DAYS_BIRTH                                        3.216099e-06
## DAYS_EMPLOYED                                     6.241027e-06
## OWN_CAR_AGE                                       1.037233e-04
## OCCUPATION_TYPEAccountants                       -1.463772e-02
## OCCUPATION_TYPECleaning staff                    -1.694824e-02
## OCCUPATION_TYPECooking staff                      1.418259e-02
## OCCUPATION_TYPECore staff                        -5.402408e-03
## OCCUPATION_TYPEDrivers                            1.028013e-02
## OCCUPATION_TYPEHigh skill tech staff             -1.774165e-02
## OCCUPATION_TYPEHR staff                          -7.604784e-03
## OCCUPATION_TYPEIT staff                          -2.806577e-02
## OCCUPATION_TYPELaborers                          -4.816502e-03
## OCCUPATION_TYPELow-skill Laborers                 2.681807e-02
## OCCUPATION_TYPEManagers                          -5.676823e-03
## OCCUPATION_TYPEMedicine staff                    -2.491503e-02
## OCCUPATION_TYPEPrivate service staff             -6.379935e-03
## OCCUPATION_TYPERealty agents                      7.190980e-02
## OCCUPATION_TYPESales staff                       -4.065929e-03
## OCCUPATION_TYPESecretaries                        4.072782e-02
## OCCUPATION_TYPESecurity staff                     4.084883e-02
## OCCUPATION_TYPEWaiters/barmen staff               1.002301e-01
## CNT_FAM_MEMBERS2                                  1.132116e-03
## CNT_FAM_MEMBERS3                                 -2.320473e-03
## CNT_FAM_MEMBERS4                                 -5.599006e-03
## CNT_FAM_MEMBERS5                                  3.401739e-02
## CNT_FAM_MEMBERS6                                  1.794479e-01
## CNT_FAM_MEMBERS7                                  8.906367e-02
## ORGANIZATION_TYPEAgriculture                      4.382414e-03
## ORGANIZATION_TYPEBank                             8.570364e-03
## ORGANIZATION_TYPEBusiness Entity Type 1          -1.214637e-03
## ORGANIZATION_TYPEBusiness Entity Type 2           3.669097e-02
## ORGANIZATION_TYPEBusiness Entity Type 3           8.953085e-03
## ORGANIZATION_TYPECleaning                         3.671069e-02
## ORGANIZATION_TYPEConstruction                     2.132647e-02
## ORGANIZATION_TYPECulture                         -5.861336e-02
## ORGANIZATION_TYPEElectricity                     -1.564123e-02
## ORGANIZATION_TYPEEmergency                       -2.077316e-02
## ORGANIZATION_TYPEGovernment                       1.162231e-03
## ORGANIZATION_TYPEHotel                           -6.344843e-02
## ORGANIZATION_TYPEHousing                          5.454882e-03
## ORGANIZATION_TYPEIndustry: type 1                -3.756790e-02
## ORGANIZATION_TYPEIndustry: type 11                1.558636e-02
## ORGANIZATION_TYPEIndustry: type 12                9.957355e-02
## ORGANIZATION_TYPEIndustry: type 13               -1.205705e-01
## ORGANIZATION_TYPEIndustry: type 2                -7.879773e-02
## ORGANIZATION_TYPEIndustry: type 3                 4.168363e-02
## ORGANIZATION_TYPEIndustry: type 4                 1.304441e-02
## ORGANIZATION_TYPEIndustry: type 5                 1.303146e-02
## ORGANIZATION_TYPEIndustry: type 6                -7.803666e-02
## ORGANIZATION_TYPEIndustry: type 7                 2.541053e-02
## ORGANIZATION_TYPEIndustry: type 9                 3.122153e-02
## ORGANIZATION_TYPEInsurance                       -7.338810e-02
## ORGANIZATION_TYPEKindergarten                    -3.629599e-03
## ORGANIZATION_TYPELegal Services                   1.982034e-02
## ORGANIZATION_TYPEMedicine                         3.709787e-02
## ORGANIZATION_TYPEMilitary                        -3.655942e-02
## ORGANIZATION_TYPEMobile                          -9.156294e-02
## ORGANIZATION_TYPEOther                            1.135235e-03
## ORGANIZATION_TYPEPolice                          -3.770840e-02
## ORGANIZATION_TYPEPostal                          -6.280312e-03
## ORGANIZATION_TYPERealtor                          2.169619e-01
## ORGANIZATION_TYPEReligion                        -3.296117e-02
## ORGANIZATION_TYPERestaurant                      -1.767501e-02
## ORGANIZATION_TYPESchool                           8.833369e-04
## ORGANIZATION_TYPESecurity                        -3.087008e-02
## ORGANIZATION_TYPESecurity Ministries             -3.000705e-03
## ORGANIZATION_TYPESelf-employed                    1.969723e-02
## ORGANIZATION_TYPEServices                        -1.091431e-02
## ORGANIZATION_TYPETelecom                         -2.065610e-02
## ORGANIZATION_TYPETrade: type 1                    1.810593e-02
## ORGANIZATION_TYPETrade: type 2                   -1.085085e-03
## ORGANIZATION_TYPETrade: type 3                   -1.529725e-02
## ORGANIZATION_TYPETrade: type 4                   -1.012080e-01
## ORGANIZATION_TYPETrade: type 6                   -9.123168e-03
## ORGANIZATION_TYPETrade: type 7                    3.726542e-02
## ORGANIZATION_TYPETransport: type 1               -7.505025e-02
## ORGANIZATION_TYPETransport: type 2                7.819173e-02
## ORGANIZATION_TYPETransport: type 3                1.384465e-01
## ORGANIZATION_TYPETransport: type 4               -3.459733e-02
## ORGANIZATION_TYPEUniversity                      -3.775120e-04
## ORGANIZATION_TYPEXNA                             -2.240731e+00
## AMR_REQ_CREDIT_BUREAU_SUM1                        4.190379e-03
## AMR_REQ_CREDIT_BUREAU_SUM2                        1.092473e-02
## AMR_REQ_CREDIT_BUREAU_SUM3                        1.288864e-02
## AMR_REQ_CREDIT_BUREAU_SUM4                        3.119740e-03
## AMR_REQ_CREDIT_BUREAU_SUM5                       -7.012164e-03
## AMR_REQ_CREDIT_BUREAU_SUM6                        2.503563e-02
## AMR_REQ_CREDIT_BUREAU_SUM7                        1.588082e-02
## AMR_REQ_CREDIT_BUREAU_SUM8                        5.843887e-02
## AMR_REQ_CREDIT_BUREAU_SUM9                        1.691654e-02
## AMR_REQ_CREDIT_BUREAU_SUM10                      -1.849285e-02
## AMR_REQ_CREDIT_BUREAU_SUM11                       3.053713e-03
## AMR_REQ_CREDIT_BUREAU_SUM12                      -4.140642e-02
## AMR_REQ_CREDIT_BUREAU_SUM13                      -7.344946e-02
## AMR_REQ_CREDIT_BUREAU_SUM14                      -1.746657e-02
## AMR_REQ_CREDIT_BUREAU_SUM15                      -8.302970e-02
## AMR_REQ_CREDIT_BUREAU_SUM16                      -2.768300e-02
## AMR_REQ_CREDIT_BUREAU_SUM17                      -9.937823e-02
## AMR_REQ_CREDIT_BUREAU_SUM18                      -7.341512e-02
## AMR_REQ_CREDIT_BUREAU_SUM19                      -1.680945e-02
## AMR_REQ_CREDIT_BUREAU_SUM20                      -5.056997e-02
## AMR_REQ_CREDIT_BUREAU_SUM28                      -1.117664e-01
##                                                     std.error    statistic
## (Intercept)                                      3.238824e-01  0.637039695
## CODE_GENDERM                                     7.349921e-03  5.922200671
## NAME_CONTRACT_TYPERevolving loans                9.804538e-03 -5.156065232
## AMT_INCOME_TOTAL                                 3.806233e-08 -1.396472747
## FLAG_OWN_CARY                                    6.335356e-03 -4.863258282
## FLAG_OWN_REALTYY                                 6.208820e-03  0.838548695
## AMT_CREDIT                                       1.107826e-08 -1.191966886
## AMT_ANNUITY                                      3.253000e-07 -0.544095497
## NAME_TYPE_SUITEChildren                          5.078004e-02 -0.542767609
## NAME_TYPE_SUITEFamily                            4.468996e-02 -0.218233848
## NAME_TYPE_SUITEGroup of people                   1.298177e-01 -0.878341280
## NAME_TYPE_SUITEOther_A                           7.422377e-02 -0.857217725
## NAME_TYPE_SUITEOther_B                           5.868265e-02 -1.044792141
## NAME_TYPE_SUITESpouse, partner                   4.628181e-02 -0.203722120
## NAME_TYPE_SUITEUnaccompanied                     4.412372e-02 -0.343197977
## NAME_INCOME_TYPECommercial associate             2.844942e-01 -0.361259798
## NAME_INCOME_TYPEPensioner                        3.946596e-01 -0.400389083
## NAME_INCOME_TYPEState servant                    2.848237e-01 -0.373563697
## NAME_INCOME_TYPEStudent                          3.901416e-01 -0.249116717
## NAME_INCOME_TYPEUnemployed                       4.799008e-01 -0.437092064
## NAME_INCOME_TYPEWorking                          2.846590e-01 -0.291949486
## NAME_EDUCATION_TYPEHigher education              1.222726e-01  0.303039512
## NAME_EDUCATION_TYPEIncomplete higher             1.231872e-01  0.441687961
## NAME_EDUCATION_TYPELower secondary               1.248090e-01  0.622387764
## NAME_EDUCATION_TYPESecondary / secondary special 1.221992e-01  0.454964697
## NAME_FAMILY_STATUSMarried                        9.324405e-03 -0.647327578
## NAME_FAMILY_STATUSSeparated                      1.701766e-02 -0.098174855
## NAME_FAMILY_STATUSSingle / not married           1.680871e-02  0.475938598
## NAME_FAMILY_STATUSWidow                          2.034232e-02  0.849372761
## NAME_HOUSING_TYPEHouse / apartment               4.101925e-02  0.143638448
## NAME_HOUSING_TYPEMunicipal apartment             4.344237e-02 -0.047212774
## NAME_HOUSING_TYPEOffice apartment                5.133026e-02 -0.879207432
## NAME_HOUSING_TYPERented apartment                4.573693e-02  0.761341852
## NAME_HOUSING_TYPEWith parents                    4.277699e-02  0.590692396
## REGION_POPULATION_RELATIVE                       2.097866e-01 -0.467410822
## DAYS_BIRTH                                       9.715712e-07  3.310204273
## DAYS_EMPLOYED                                    1.476965e-06  4.225575963
## OWN_CAR_AGE                                      2.801423e-04  0.370252048
## OCCUPATION_TYPEAccountants                       1.778898e-02 -0.822853488
## OCCUPATION_TYPECleaning staff                    2.441296e-02 -0.694231084
## OCCUPATION_TYPECooking staff                     2.101915e-02  0.674746192
## OCCUPATION_TYPECore staff                        1.324128e-02 -0.407997419
## OCCUPATION_TYPEDrivers                           1.444968e-02  0.711443845
## OCCUPATION_TYPEHigh skill tech staff             1.559747e-02 -1.137469734
## OCCUPATION_TYPEHR staff                          7.360465e-02 -0.103319349
## OCCUPATION_TYPEIT staff                          6.037217e-02 -0.464879335
## OCCUPATION_TYPELaborers                          1.040000e-02 -0.463125263
## OCCUPATION_TYPELow-skill Laborers                3.512250e-02  0.763558124
## OCCUPATION_TYPEManagers                          1.342852e-02 -0.422743750
## OCCUPATION_TYPEMedicine staff                    2.288624e-02 -1.088646858
## OCCUPATION_TYPEPrivate service staff             3.046778e-02 -0.209399389
## OCCUPATION_TYPERealty agents                     5.679346e-02  1.266163399
## OCCUPATION_TYPESales staff                       1.227742e-02 -0.331171413
## OCCUPATION_TYPESecretaries                       4.763194e-02  0.855052630
## OCCUPATION_TYPESecurity staff                    2.237480e-02  1.825662323
## OCCUPATION_TYPEWaiters/barmen staff              4.716424e-02  2.125129651
## CNT_FAM_MEMBERS2                                 1.464121e-02  0.077323907
## CNT_FAM_MEMBERS3                                 1.639724e-02 -0.141516061
## CNT_FAM_MEMBERS4                                 1.817850e-02 -0.308001501
## CNT_FAM_MEMBERS5                                 3.137761e-02  1.084129310
## CNT_FAM_MEMBERS6                                 8.416156e-02  2.132183844
## CNT_FAM_MEMBERS7                                 1.237411e-01  0.719758220
## ORGANIZATION_TYPEAgriculture                     7.110094e-02  0.061636519
## ORGANIZATION_TYPEBank                            7.345444e-02  0.116675911
## ORGANIZATION_TYPEBusiness Entity Type 1          6.733533e-02 -0.018038620
## ORGANIZATION_TYPEBusiness Entity Type 2          6.608540e-02  0.555205375
## ORGANIZATION_TYPEBusiness Entity Type 3          6.467652e-02  0.138428667
## ORGANIZATION_TYPECleaning                        1.114015e-01  0.329534866
## ORGANIZATION_TYPEConstruction                    6.707111e-02  0.317968028
## ORGANIZATION_TYPECulture                         9.999669e-02 -0.586153024
## ORGANIZATION_TYPEElectricity                     8.055834e-02 -0.194160226
## ORGANIZATION_TYPEEmergency                       8.792009e-02 -0.236273141
## ORGANIZATION_TYPEGovernment                      6.604658e-02  0.017597149
## ORGANIZATION_TYPEHotel                           8.257608e-02 -0.768363320
## ORGANIZATION_TYPEHousing                         6.982835e-02  0.078118440
## ORGANIZATION_TYPEIndustry: type 1                7.841714e-02 -0.479077683
## ORGANIZATION_TYPEIndustry: type 11               7.098229e-02  0.219580928
## ORGANIZATION_TYPEIndustry: type 12               1.020855e-01  0.975393521
## ORGANIZATION_TYPEIndustry: type 13               2.036842e-01 -0.591948495
## ORGANIZATION_TYPEIndustry: type 2                9.731529e-02 -0.809715791
## ORGANIZATION_TYPEIndustry: type 3                6.914345e-02  0.602857174
## ORGANIZATION_TYPEIndustry: type 4                7.817763e-02  0.166856096
## ORGANIZATION_TYPEIndustry: type 5                8.878250e-02  0.146779649
## ORGANIZATION_TYPEIndustry: type 6                1.380145e-01 -0.565423688
## ORGANIZATION_TYPEIndustry: type 7                7.682659e-02  0.330751780
## ORGANIZATION_TYPEIndustry: type 9                6.945843e-02  0.449499499
## ORGANIZATION_TYPEInsurance                       9.119419e-02 -0.804745374
## ORGANIZATION_TYPEKindergarten                    6.709661e-02 -0.054095110
## ORGANIZATION_TYPELegal Services                  9.761973e-02  0.203036166
## ORGANIZATION_TYPEMedicine                        6.714712e-02  0.552486377
## ORGANIZATION_TYPEMilitary                        7.111523e-02 -0.514086997
## ORGANIZATION_TYPEMobile                          1.046480e-01 -0.874961544
## ORGANIZATION_TYPEOther                           6.550997e-02  0.017329199
## ORGANIZATION_TYPEPolice                          7.237334e-02 -0.521026129
## ORGANIZATION_TYPEPostal                          7.224699e-02 -0.086928360
## ORGANIZATION_TYPERealtor                         1.045732e-01  2.074736910
## ORGANIZATION_TYPEReligion                        2.033270e-01 -0.162109178
## ORGANIZATION_TYPERestaurant                      7.372632e-02 -0.239738171
## ORGANIZATION_TYPESchool                          6.659657e-02  0.013263999
## ORGANIZATION_TYPESecurity                        7.165784e-02 -0.430798349
## ORGANIZATION_TYPESecurity Ministries             7.303849e-02 -0.041083890
## ORGANIZATION_TYPESelf-employed                   6.488590e-02  0.303567190
## ORGANIZATION_TYPEServices                        7.689387e-02 -0.141939911
## ORGANIZATION_TYPETelecom                         8.996517e-02 -0.229601124
## ORGANIZATION_TYPETrade: type 1                   1.045225e-01  0.173225100
## ORGANIZATION_TYPETrade: type 2                   7.420166e-02 -0.014623457
## ORGANIZATION_TYPETrade: type 3                   6.905253e-02 -0.221530686
## ORGANIZATION_TYPETrade: type 4                   2.036165e-01 -0.497052183
## ORGANIZATION_TYPETrade: type 6                   8.610919e-02 -0.105948836
## ORGANIZATION_TYPETrade: type 7                   6.674541e-02  0.558321819
## ORGANIZATION_TYPETransport: type 1               1.510872e-01 -0.496734785
## ORGANIZATION_TYPETransport: type 2               7.187849e-02  1.087832087
## ORGANIZATION_TYPETransport: type 3               7.668451e-02  1.805403139
## ORGANIZATION_TYPETransport: type 4               6.837164e-02 -0.506018664
## ORGANIZATION_TYPEUniversity                      7.928623e-02 -0.004761382
## ORGANIZATION_TYPEXNA                             6.089517e-01 -3.679653516
## AMR_REQ_CREDIT_BUREAU_SUM1                       9.127409e-03  0.459098434
## AMR_REQ_CREDIT_BUREAU_SUM2                       9.816924e-03  1.112846530
## AMR_REQ_CREDIT_BUREAU_SUM3                       1.053364e-02  1.223569960
## AMR_REQ_CREDIT_BUREAU_SUM4                       1.161135e-02  0.268680246
## AMR_REQ_CREDIT_BUREAU_SUM5                       1.360600e-02 -0.515373121
## AMR_REQ_CREDIT_BUREAU_SUM6                       1.725497e-02  1.450922280
## AMR_REQ_CREDIT_BUREAU_SUM7                       1.898283e-02  0.836588629
## AMR_REQ_CREDIT_BUREAU_SUM8                       2.739782e-02  2.132974918
## AMR_REQ_CREDIT_BUREAU_SUM9                       3.608539e-02  0.468792048
## AMR_REQ_CREDIT_BUREAU_SUM10                      4.468145e-02 -0.413882025
## AMR_REQ_CREDIT_BUREAU_SUM11                      7.004005e-02  0.043599527
## AMR_REQ_CREDIT_BUREAU_SUM12                      1.057255e-01 -0.391640785
## AMR_REQ_CREDIT_BUREAU_SUM13                      1.157125e-01 -0.634758237
## AMR_REQ_CREDIT_BUREAU_SUM14                      1.799169e-01 -0.097081323
## AMR_REQ_CREDIT_BUREAU_SUM15                      1.321943e-01 -0.628088210
## AMR_REQ_CREDIT_BUREAU_SUM16                      2.742893e-01 -0.100926295
## AMR_REQ_CREDIT_BUREAU_SUM17                      2.726974e-01 -0.364426748
## AMR_REQ_CREDIT_BUREAU_SUM18                      1.840078e-01 -0.398978291
## AMR_REQ_CREDIT_BUREAU_SUM19                      2.746293e-01 -0.061207775
## AMR_REQ_CREDIT_BUREAU_SUM20                      2.562267e-01 -0.197364150
## AMR_REQ_CREDIT_BUREAU_SUM28                      2.599306e-01 -0.429985680
##                                                          df      p.value
## (Intercept)                                      9842.13067 5.241138e-01
## CODE_GENDERM                                     9847.07017 3.282767e-09
## NAME_CONTRACT_TYPERevolving loans                9667.51517 2.570858e-07
## AMT_INCOME_TOTAL                                 9030.00715 1.626036e-01
## FLAG_OWN_CARY                                    9859.54073 1.172539e-06
## FLAG_OWN_REALTYY                                 9757.98866 4.017429e-01
## AMT_CREDIT                                       9807.46426 2.333029e-01
## AMT_ANNUITY                                      9841.48892 5.863881e-01
## NAME_TYPE_SUITEChildren                          9835.74541 5.873021e-01
## NAME_TYPE_SUITEFamily                            9851.82268 8.272514e-01
## NAME_TYPE_SUITEGroup of people                   9854.89304 3.797799e-01
## NAME_TYPE_SUITEOther_A                           9856.74596 3.913454e-01
## NAME_TYPE_SUITEOther_B                           9853.70456 2.961447e-01
## NAME_TYPE_SUITESpouse, partner                   9854.28935 8.385749e-01
## NAME_TYPE_SUITEUnaccompanied                     9846.19867 7.314568e-01
## NAME_INCOME_TYPECommercial associate             9845.81218 7.179130e-01
## NAME_INCOME_TYPEPensioner                        9850.37841 6.888786e-01
## NAME_INCOME_TYPEState servant                    9846.11085 7.087370e-01
## NAME_INCOME_TYPEStudent                          9858.09436 8.032757e-01
## NAME_INCOME_TYPEUnemployed                       9852.56370 6.620542e-01
## NAME_INCOME_TYPEWorking                          9845.49705 7.703314e-01
## NAME_EDUCATION_TYPEHigher education              9857.85728 7.618661e-01
## NAME_EDUCATION_TYPEIncomplete higher             9857.92865 6.587247e-01
## NAME_EDUCATION_TYPELower secondary               9856.82413 5.337013e-01
## NAME_EDUCATION_TYPESecondary / secondary special 9858.02816 6.491447e-01
## NAME_FAMILY_STATUSMarried                        9851.19907 5.174350e-01
## NAME_FAMILY_STATUSSeparated                      9852.71466 9.217954e-01
## NAME_FAMILY_STATUSSingle / not married           9855.64716 6.341287e-01
## NAME_FAMILY_STATUSWidow                          9801.82300 3.956945e-01
## NAME_HOUSING_TYPEHouse / apartment               9850.15576 8.857889e-01
## NAME_HOUSING_TYPEMunicipal apartment             9852.04325 9.623446e-01
## NAME_HOUSING_TYPEOffice apartment                9855.89066 3.793102e-01
## NAME_HOUSING_TYPERented apartment                9857.25008 4.464711e-01
## NAME_HOUSING_TYPEWith parents                    9853.18499 5.547401e-01
## REGION_POPULATION_RELATIVE                       9810.09843 6.402163e-01
## DAYS_BIRTH                                       9852.36184 9.356256e-04
## DAYS_EMPLOYED                                    9851.66253 2.404757e-05
## OWN_CAR_AGE                                        62.44924 7.112027e-01
## OCCUPATION_TYPEAccountants                       9795.14360 4.106112e-01
## OCCUPATION_TYPECleaning staff                    9849.07232 4.875537e-01
## OCCUPATION_TYPECooking staff                     9859.54353 4.998529e-01
## OCCUPATION_TYPECore staff                        9857.26052 6.832844e-01
## OCCUPATION_TYPEDrivers                           9849.62182 4.768261e-01
## OCCUPATION_TYPEHigh skill tech staff             9857.56269 2.553696e-01
## OCCUPATION_TYPEHR staff                          9845.41388 9.177116e-01
## OCCUPATION_TYPEIT staff                          9852.56666 6.420281e-01
## OCCUPATION_TYPELaborers                          9838.74883 6.432848e-01
## OCCUPATION_TYPELow-skill Laborers                9839.07310 4.451489e-01
## OCCUPATION_TYPEManagers                          9854.13464 6.724915e-01
## OCCUPATION_TYPEMedicine staff                    9845.39368 2.763363e-01
## OCCUPATION_TYPEPrivate service staff             9857.24427 8.341408e-01
## OCCUPATION_TYPERealty agents                     9858.84639 2.054845e-01
## OCCUPATION_TYPESales staff                       9859.23310 7.405221e-01
## OCCUPATION_TYPESecretaries                       9855.14485 3.925428e-01
## OCCUPATION_TYPESecurity staff                    9850.20513 6.793137e-02
## OCCUPATION_TYPEWaiters/barmen staff              9857.58809 3.360059e-02
## CNT_FAM_MEMBERS2                                 9857.49802 9.383674e-01
## CNT_FAM_MEMBERS3                                 9858.30430 8.874652e-01
## CNT_FAM_MEMBERS4                                 9859.79692 7.580877e-01
## CNT_FAM_MEMBERS5                                 9859.60681 2.783340e-01
## CNT_FAM_MEMBERS6                                 9859.79036 3.301638e-02
## CNT_FAM_MEMBERS7                                 9859.90649 4.716909e-01
## ORGANIZATION_TYPEAgriculture                     9851.96946 9.508535e-01
## ORGANIZATION_TYPEBank                            9853.09656 9.071193e-01
## ORGANIZATION_TYPEBusiness Entity Type 1          9849.95852 9.856084e-01
## ORGANIZATION_TYPEBusiness Entity Type 2          9852.76155 5.787668e-01
## ORGANIZATION_TYPEBusiness Entity Type 3          9849.04393 8.899045e-01
## ORGANIZATION_TYPECleaning                        9852.78481 7.417584e-01
## ORGANIZATION_TYPEConstruction                    9846.97936 7.505159e-01
## ORGANIZATION_TYPECulture                         9855.28352 5.577861e-01
## ORGANIZATION_TYPEElectricity                     9855.34649 8.460544e-01
## ORGANIZATION_TYPEEmergency                       9854.58245 8.132256e-01
## ORGANIZATION_TYPEGovernment                      9849.30070 9.859606e-01
## ORGANIZATION_TYPEHotel                           9856.82784 4.422898e-01
## ORGANIZATION_TYPEHousing                         9853.55464 9.377354e-01
## ORGANIZATION_TYPEIndustry: type 1                9849.99755 6.318940e-01
## ORGANIZATION_TYPEIndustry: type 11               9848.57262 8.262021e-01
## ORGANIZATION_TYPEIndustry: type 12               9856.01441 3.293890e-01
## ORGANIZATION_TYPEIndustry: type 13               9852.40309 5.538987e-01
## ORGANIZATION_TYPEIndustry: type 2                9856.14096 4.181231e-01
## ORGANIZATION_TYPEIndustry: type 3                9849.73606 5.466176e-01
## ORGANIZATION_TYPEIndustry: type 4                9852.96031 8.674867e-01
## ORGANIZATION_TYPEIndustry: type 5                9855.15334 8.833089e-01
## ORGANIZATION_TYPEIndustry: type 6                9857.90329 5.717985e-01
## ORGANIZATION_TYPEIndustry: type 7                9848.23792 7.408390e-01
## ORGANIZATION_TYPEIndustry: type 9                9851.75824 6.530812e-01
## ORGANIZATION_TYPEInsurance                       9853.96224 4.209860e-01
## ORGANIZATION_TYPEKindergarten                    9850.80883 9.568605e-01
## ORGANIZATION_TYPELegal Services                  9853.80085 8.391110e-01
## ORGANIZATION_TYPEMedicine                        9851.10286 5.806277e-01
## ORGANIZATION_TYPEMilitary                        9853.58736 6.072027e-01
## ORGANIZATION_TYPEMobile                          9857.00555 3.816161e-01
## ORGANIZATION_TYPEOther                           9850.35832 9.861743e-01
## ORGANIZATION_TYPEPolice                          9852.53848 6.023603e-01
## ORGANIZATION_TYPEPostal                          9845.47055 9.307302e-01
## ORGANIZATION_TYPERealtor                         9857.75687 3.803680e-02
## ORGANIZATION_TYPEReligion                        9858.83227 8.712232e-01
## ORGANIZATION_TYPERestaurant                      9849.14060 8.105382e-01
## ORGANIZATION_TYPESchool                          9850.11849 9.894174e-01
## ORGANIZATION_TYPESecurity                        9851.16020 6.666244e-01
## ORGANIZATION_TYPESecurity Ministries             9849.88328 9.672298e-01
## ORGANIZATION_TYPESelf-employed                   9850.71347 7.614641e-01
## ORGANIZATION_TYPEServices                        9855.21952 8.871304e-01
## ORGANIZATION_TYPETelecom                         9855.53402 8.184065e-01
## ORGANIZATION_TYPETrade: type 1                   9847.12069 8.624781e-01
## ORGANIZATION_TYPETrade: type 2                   9850.05884 9.883329e-01
## ORGANIZATION_TYPETrade: type 3                   9842.19071 8.246838e-01
## ORGANIZATION_TYPETrade: type 4                   9858.41253 6.191633e-01
## ORGANIZATION_TYPETrade: type 6                   9850.30181 9.156251e-01
## ORGANIZATION_TYPETrade: type 7                   9848.48774 5.766373e-01
## ORGANIZATION_TYPETransport: type 1               9857.65978 6.193872e-01
## ORGANIZATION_TYPETransport: type 2               9852.87199 2.766958e-01
## ORGANIZATION_TYPETransport: type 3               9854.21526 7.104211e-02
## ORGANIZATION_TYPETransport: type 4               9849.40373 6.128549e-01
## ORGANIZATION_TYPEUniversity                      9851.63858 9.962011e-01
## ORGANIZATION_TYPEXNA                             9855.66774 2.347956e-04
## AMR_REQ_CREDIT_BUREAU_SUM1                       1417.16708 6.461736e-01
## AMR_REQ_CREDIT_BUREAU_SUM2                        250.28318 2.658015e-01
## AMR_REQ_CREDIT_BUREAU_SUM3                        260.84440 2.211437e-01
## AMR_REQ_CREDIT_BUREAU_SUM4                        311.38428 7.881814e-01
## AMR_REQ_CREDIT_BUREAU_SUM5                        499.95796 6.063039e-01
## AMR_REQ_CREDIT_BUREAU_SUM6                        172.24968 1.468333e-01
## AMR_REQ_CREDIT_BUREAU_SUM7                        521.47606 4.028441e-01
## AMR_REQ_CREDIT_BUREAU_SUM8                        302.47679 3.295141e-02
## AMR_REQ_CREDIT_BUREAU_SUM9                        276.96744 6.392286e-01
## AMR_REQ_CREDIT_BUREAU_SUM10                      6861.17223 6.789695e-01
## AMR_REQ_CREDIT_BUREAU_SUM11                      1787.91692 9.652245e-01
## AMR_REQ_CREDIT_BUREAU_SUM12                      9659.66009 6.953321e-01
## AMR_REQ_CREDIT_BUREAU_SUM13                      9710.15537 5.256008e-01
## AMR_REQ_CREDIT_BUREAU_SUM14                      9819.14406 9.226638e-01
## AMR_REQ_CREDIT_BUREAU_SUM15                      9501.72835 5.299607e-01
## AMR_REQ_CREDIT_BUREAU_SUM16                      9859.90649 9.196110e-01
## AMR_REQ_CREDIT_BUREAU_SUM17                      9859.35814 7.155472e-01
## AMR_REQ_CREDIT_BUREAU_SUM18                      9491.65927 6.899178e-01
## AMR_REQ_CREDIT_BUREAU_SUM19                      9859.90649 9.511950e-01
## AMR_REQ_CREDIT_BUREAU_SUM20                      9858.65550 8.435467e-01
## AMR_REQ_CREDIT_BUREAU_SUM28                      9833.27900 6.672155e-01
##                                                          2.5 %
## (Intercept)                                      -4.285499e-01
## CODE_GENDERM                                      2.912035e-02
## NAME_CONTRACT_TYPERevolving loans                -6.977179e-02
## AMT_INCOME_TOTAL                                 -1.277638e-07
## FLAG_OWN_CARY                                    -4.322907e-02
## FLAG_OWN_REALTYY                                 -6.964175e-03
## AMT_CREDIT                                       -3.492059e-08
## AMT_ANNUITY                                      -8.146490e-07
## NAME_TYPE_SUITEChildren                          -1.271011e-01
## NAME_TYPE_SUITEFamily                            -9.735433e-02
## NAME_TYPE_SUITEGroup of people                   -3.684936e-01
## NAME_TYPE_SUITEOther_A                           -2.091197e-01
## NAME_TYPE_SUITEOther_B                           -1.763412e-01
## NAME_TYPE_SUITESpouse, partner                   -1.001504e-01
## NAME_TYPE_SUITEUnaccompanied                     -1.016347e-01
## NAME_INCOME_TYPECommercial associate             -6.604432e-01
## NAME_INCOME_TYPEPensioner                        -9.316310e-01
## NAME_INCOME_TYPEState servant                    -6.647126e-01
## NAME_INCOME_TYPEStudent                          -8.619482e-01
## NAME_INCOME_TYPEUnemployed                       -1.150465e+00
## NAME_INCOME_TYPEWorking                          -6.410961e-01
## NAME_EDUCATION_TYPEHigher education              -2.026260e-01
## NAME_EDUCATION_TYPEIncomplete higher             -1.870618e-01
## NAME_EDUCATION_TYPELower secondary               -1.669716e-01
## NAME_EDUCATION_TYPESecondary / secondary special -1.839391e-01
## NAME_FAMILY_STATUSMarried                        -2.431369e-02
## NAME_FAMILY_STATUSSeparated                      -3.502881e-02
## NAME_FAMILY_STATUSSingle / not married           -2.494859e-02
## NAME_FAMILY_STATUSWidow                          -2.259693e-02
## NAME_HOUSING_TYPEHouse / apartment               -7.451419e-02
## NAME_HOUSING_TYPEMunicipal apartment             -8.720697e-02
## NAME_HOUSING_TYPEOffice apartment                -1.457478e-01
## NAME_HOUSING_TYPERented apartment                -5.483231e-02
## NAME_HOUSING_TYPEWith parents                    -5.858361e-02
## REGION_POPULATION_RELATIVE                       -5.092816e-01
## DAYS_BIRTH                                        1.311621e-06
## DAYS_EMPLOYED                                     3.345873e-06
## OWN_CAR_AGE                                      -4.561931e-04
## OCCUPATION_TYPEAccountants                       -4.950778e-02
## OCCUPATION_TYPECleaning staff                    -6.480265e-02
## OCCUPATION_TYPECooking staff                     -2.701925e-02
## OCCUPATION_TYPECore staff                        -3.135803e-02
## OCCUPATION_TYPEDrivers                           -1.804419e-02
## OCCUPATION_TYPEHigh skill tech staff             -4.831588e-02
## OCCUPATION_TYPEHR staff                          -1.518850e-01
## OCCUPATION_TYPEIT staff                          -1.464076e-01
## OCCUPATION_TYPELaborers                          -2.520263e-02
## OCCUPATION_TYPELow-skill Laborers                -4.202923e-02
## OCCUPATION_TYPEManagers                          -3.199947e-02
## OCCUPATION_TYPEMedicine staff                    -6.977675e-02
## OCCUPATION_TYPEPrivate service staff             -6.610302e-02
## OCCUPATION_TYPERealty agents                     -3.941700e-02
## OCCUPATION_TYPESales staff                       -2.813218e-02
## OCCUPATION_TYPESecretaries                       -5.264054e-02
## OCCUPATION_TYPESecurity staff                    -3.010362e-03
## OCCUPATION_TYPEWaiters/barmen staff               7.778561e-03
## CNT_FAM_MEMBERS2                                 -2.756765e-02
## CNT_FAM_MEMBERS3                                 -3.446242e-02
## CNT_FAM_MEMBERS4                                 -4.123259e-02
## CNT_FAM_MEMBERS5                                 -2.748915e-02
## CNT_FAM_MEMBERS6                                  1.447404e-02
## CNT_FAM_MEMBERS7                                 -1.534942e-01
## ORGANIZATION_TYPEAgriculture                     -1.349900e-01
## ORGANIZATION_TYPEBank                            -1.354154e-01
## ORGANIZATION_TYPEBusiness Entity Type 1          -1.332057e-01
## ORGANIZATION_TYPEBusiness Entity Type 2          -9.284995e-02
## ORGANIZATION_TYPEBusiness Entity Type 3          -1.178262e-01
## ORGANIZATION_TYPECleaning                        -1.816591e-01
## ORGANIZATION_TYPEConstruction                    -1.101466e-01
## ORGANIZATION_TYPECulture                         -2.546274e-01
## ORGANIZATION_TYPEElectricity                     -1.735521e-01
## ORGANIZATION_TYPEEmergency                       -1.931145e-01
## ORGANIZATION_TYPEGovernment                      -1.283026e-01
## ORGANIZATION_TYPEHotel                           -2.253144e-01
## ORGANIZATION_TYPEHousing                         -1.314230e-01
## ORGANIZATION_TYPEIndustry: type 1                -1.912816e-01
## ORGANIZATION_TYPEIndustry: type 11               -1.235535e-01
## ORGANIZATION_TYPEIndustry: type 12               -1.005350e-01
## ORGANIZATION_TYPEIndustry: type 13               -5.198332e-01
## ORGANIZATION_TYPEIndustry: type 2                -2.695556e-01
## ORGANIZATION_TYPEIndustry: type 3                -9.385171e-02
## ORGANIZATION_TYPEIndustry: type 4                -1.401997e-01
## ORGANIZATION_TYPEIndustry: type 5                -1.610004e-01
## ORGANIZATION_TYPEIndustry: type 6                -3.485733e-01
## ORGANIZATION_TYPEIndustry: type 7                -1.251853e-01
## ORGANIZATION_TYPEIndustry: type 9                -1.049312e-01
## ORGANIZATION_TYPEInsurance                       -2.521474e-01
## ORGANIZATION_TYPEKindergarten                    -1.351527e-01
## ORGANIZATION_TYPELegal Services                  -1.715343e-01
## ORGANIZATION_TYPEMedicine                        -9.452424e-02
## ORGANIZATION_TYPEMilitary                        -1.759598e-01
## ORGANIZATION_TYPEMobile                          -2.966944e-01
## ORGANIZATION_TYPEOther                           -1.272777e-01
## ORGANIZATION_TYPEPolice                          -1.795750e-01
## ORGANIZATION_TYPEPostal                          -1.478992e-01
## ORGANIZATION_TYPERealtor                          1.197700e-02
## ORGANIZATION_TYPEReligion                        -4.315237e-01
## ORGANIZATION_TYPERestaurant                      -1.621937e-01
## ORGANIZATION_TYPESchool                          -1.296596e-01
## ORGANIZATION_TYPESecurity                        -1.713341e-01
## ORGANIZATION_TYPESecurity Ministries             -1.461711e-01
## ORGANIZATION_TYPESelf-employed                   -1.074924e-01
## ORGANIZATION_TYPEServices                        -1.616420e-01
## ORGANIZATION_TYPETelecom                         -1.970062e-01
## ORGANIZATION_TYPETrade: type 1                   -1.867797e-01
## ORGANIZATION_TYPETrade: type 2                   -1.465355e-01
## ORGANIZATION_TYPETrade: type 3                   -1.506544e-01
## ORGANIZATION_TYPETrade: type 4                   -5.003380e-01
## ORGANIZATION_TYPETrade: type 6                   -1.779148e-01
## ORGANIZATION_TYPETrade: type 7                   -9.356927e-02
## ORGANIZATION_TYPETransport: type 1               -3.712120e-01
## ORGANIZATION_TYPETransport: type 2               -6.270483e-02
## ORGANIZATION_TYPETransport: type 3               -1.187089e-02
## ORGANIZATION_TYPETransport: type 4               -1.686197e-01
## ORGANIZATION_TYPEUniversity                      -1.557948e-01
## ORGANIZATION_TYPEXNA                             -3.434401e+00
## AMR_REQ_CREDIT_BUREAU_SUM1                       -1.371431e-02
## AMR_REQ_CREDIT_BUREAU_SUM2                       -8.409579e-03
## AMR_REQ_CREDIT_BUREAU_SUM3                       -7.853147e-03
## AMR_REQ_CREDIT_BUREAU_SUM4                       -1.972689e-02
## AMR_REQ_CREDIT_BUREAU_SUM5                       -3.374414e-02
## AMR_REQ_CREDIT_BUREAU_SUM6                       -9.022792e-03
## AMR_REQ_CREDIT_BUREAU_SUM7                       -2.141140e-02
## AMR_REQ_CREDIT_BUREAU_SUM8                        4.524399e-03
## AMR_REQ_CREDIT_BUREAU_SUM9                       -5.411993e-02
## AMR_REQ_CREDIT_BUREAU_SUM10                      -1.060823e-01
## AMR_REQ_CREDIT_BUREAU_SUM11                      -1.343153e-01
## AMR_REQ_CREDIT_BUREAU_SUM12                      -2.486506e-01
## AMR_REQ_CREDIT_BUREAU_SUM13                      -3.002701e-01
## AMR_REQ_CREDIT_BUREAU_SUM14                      -3.701406e-01
## AMR_REQ_CREDIT_BUREAU_SUM15                      -3.421588e-01
## AMR_REQ_CREDIT_BUREAU_SUM16                      -5.653461e-01
## AMR_REQ_CREDIT_BUREAU_SUM17                      -6.339209e-01
## AMR_REQ_CREDIT_BUREAU_SUM18                      -4.341098e-01
## AMR_REQ_CREDIT_BUREAU_SUM19                      -5.551390e-01
## AMR_REQ_CREDIT_BUREAU_SUM20                      -5.528268e-01
## AMR_REQ_CREDIT_BUREAU_SUM28                      -6.212837e-01
##                                                         97.5 %
## (Intercept)                                       8.412018e-01
## CODE_GENDERM                                      5.793506e-02
## NAME_CONTRACT_TYPERevolving loans                -3.133389e-02
## AMT_INCOME_TOTAL                                  2.145779e-08
## FLAG_OWN_CARY                                    -1.839188e-02
## FLAG_OWN_REALTYY                                  1.737697e-02
## AMT_CREDIT                                        8.510751e-09
## AMT_ANNUITY                                       4.606605e-07
## NAME_TYPE_SUITEChildren                           7.197754e-02
## NAME_TYPE_SUITEFamily                             7.784861e-02
## NAME_TYPE_SUITEGroup of people                    1.404451e-01
## NAME_TYPE_SUITEOther_A                            8.186785e-02
## NAME_TYPE_SUITEOther_B                            5.371884e-02
## NAME_TYPE_SUITESpouse, partner                    8.129319e-02
## NAME_TYPE_SUITEUnaccompanied                      7.134837e-02
## NAME_INCOME_TYPECommercial associate              4.548906e-01
## NAME_INCOME_TYPEPensioner                         6.155963e-01
## NAME_INCOME_TYPEState servant                     4.519130e-01
## NAME_INCOME_TYPEStudent                           6.675666e-01
## NAME_INCOME_TYPEUnemployed                        7.309430e-01
## NAME_INCOME_TYPEWorking                           4.748840e-01
## NAME_EDUCATION_TYPEHigher education               2.767328e-01
## NAME_EDUCATION_TYPEIncomplete higher              2.958824e-01
## NAME_EDUCATION_TYPELower secondary                3.223308e-01
## NAME_EDUCATION_TYPESecondary / secondary special  2.951317e-01
## NAME_FAMILY_STATUSMarried                         1.224180e-02
## NAME_FAMILY_STATUSSeparated                       3.168740e-02
## NAME_FAMILY_STATUSSingle / not married            4.094842e-02
## NAME_FAMILY_STATUSWidow                           5.715336e-02
## NAME_HOUSING_TYPEHouse / apartment                8.629808e-02
## NAME_HOUSING_TYPEMunicipal apartment              8.310490e-02
## NAME_HOUSING_TYPEOffice apartment                 5.548787e-02
## NAME_HOUSING_TYPERented apartment                 1.244752e-01
## NAME_HOUSING_TYPEWith parents                     1.091197e-01
## REGION_POPULATION_RELATIVE                        3.131685e-01
## DAYS_BIRTH                                        5.120578e-06
## DAYS_EMPLOYED                                     9.136180e-06
## OWN_CAR_AGE                                       6.636396e-04
## OCCUPATION_TYPEAccountants                        2.023234e-02
## OCCUPATION_TYPECleaning staff                     3.090617e-02
## OCCUPATION_TYPECooking staff                      5.538443e-02
## OCCUPATION_TYPECore staff                         2.055321e-02
## OCCUPATION_TYPEDrivers                            3.860446e-02
## OCCUPATION_TYPEHigh skill tech staff              1.283258e-02
## OCCUPATION_TYPEHR staff                           1.366754e-01
## OCCUPATION_TYPEIT staff                           9.027604e-02
## OCCUPATION_TYPELaborers                           1.556963e-02
## OCCUPATION_TYPELow-skill Laborers                 9.566536e-02
## OCCUPATION_TYPEManagers                           2.064583e-02
## OCCUPATION_TYPEMedicine staff                     1.994669e-02
## OCCUPATION_TYPEPrivate service staff              5.334315e-02
## OCCUPATION_TYPERealty agents                      1.832366e-01
## OCCUPATION_TYPESales staff                        2.000032e-02
## OCCUPATION_TYPESecretaries                        1.340962e-01
## OCCUPATION_TYPESecurity staff                     8.470802e-02
## OCCUPATION_TYPEWaiters/barmen staff               1.926817e-01
## CNT_FAM_MEMBERS2                                  2.983188e-02
## CNT_FAM_MEMBERS3                                  2.982147e-02
## CNT_FAM_MEMBERS4                                  3.003458e-02
## CNT_FAM_MEMBERS5                                  9.552393e-02
## CNT_FAM_MEMBERS6                                  3.444218e-01
## CNT_FAM_MEMBERS7                                  3.316215e-01
## ORGANIZATION_TYPEAgriculture                      1.437548e-01
## ORGANIZATION_TYPEBank                             1.525561e-01
## ORGANIZATION_TYPEBusiness Entity Type 1           1.307764e-01
## ORGANIZATION_TYPEBusiness Entity Type 2           1.662319e-01
## ORGANIZATION_TYPEBusiness Entity Type 3           1.357323e-01
## ORGANIZATION_TYPECleaning                         2.550805e-01
## ORGANIZATION_TYPEConstruction                     1.527996e-01
## ORGANIZATION_TYPECulture                          1.374006e-01
## ORGANIZATION_TYPEElectricity                      1.422696e-01
## ORGANIZATION_TYPEEmergency                        1.515682e-01
## ORGANIZATION_TYPEGovernment                       1.306271e-01
## ORGANIZATION_TYPEHotel                            9.841759e-02
## ORGANIZATION_TYPEHousing                          1.423328e-01
## ORGANIZATION_TYPEIndustry: type 1                 1.161458e-01
## ORGANIZATION_TYPEIndustry: type 11                1.547262e-01
## ORGANIZATION_TYPEIndustry: type 12                2.996820e-01
## ORGANIZATION_TYPEIndustry: type 13                2.786922e-01
## ORGANIZATION_TYPEIndustry: type 2                 1.119602e-01
## ORGANIZATION_TYPEIndustry: type 3                 1.772190e-01
## ORGANIZATION_TYPEIndustry: type 4                 1.662886e-01
## ORGANIZATION_TYPEIndustry: type 5                 1.870633e-01
## ORGANIZATION_TYPEIndustry: type 6                 1.925000e-01
## ORGANIZATION_TYPEIndustry: type 7                 1.760064e-01
## ORGANIZATION_TYPEIndustry: type 9                 1.673743e-01
## ORGANIZATION_TYPEInsurance                        1.053712e-01
## ORGANIZATION_TYPEKindergarten                     1.278935e-01
## ORGANIZATION_TYPELegal Services                   2.111750e-01
## ORGANIZATION_TYPEMedicine                         1.687200e-01
## ORGANIZATION_TYPEMilitary                         1.028410e-01
## ORGANIZATION_TYPEMobile                           1.135685e-01
## ORGANIZATION_TYPEOther                            1.295482e-01
## ORGANIZATION_TYPEPolice                           1.041582e-01
## ORGANIZATION_TYPEPostal                           1.353386e-01
## ORGANIZATION_TYPERealtor                          4.219468e-01
## ORGANIZATION_TYPEReligion                         3.656013e-01
## ORGANIZATION_TYPERestaurant                       1.268437e-01
## ORGANIZATION_TYPESchool                           1.314263e-01
## ORGANIZATION_TYPESecurity                         1.095940e-01
## ORGANIZATION_TYPESecurity Ministries              1.401697e-01
## ORGANIZATION_TYPESelf-employed                    1.468869e-01
## ORGANIZATION_TYPEServices                         1.398134e-01
## ORGANIZATION_TYPETelecom                          1.556940e-01
## ORGANIZATION_TYPETrade: type 1                    2.229915e-01
## ORGANIZATION_TYPETrade: type 2                    1.443654e-01
## ORGANIZATION_TYPETrade: type 3                    1.200599e-01
## ORGANIZATION_TYPETrade: type 4                    2.979220e-01
## ORGANIZATION_TYPETrade: type 6                    1.596685e-01
## ORGANIZATION_TYPETrade: type 7                    1.681001e-01
## ORGANIZATION_TYPETransport: type 1                2.211115e-01
## ORGANIZATION_TYPETransport: type 2                2.190883e-01
## ORGANIZATION_TYPETransport: type 3                2.887638e-01
## ORGANIZATION_TYPETransport: type 4                9.942510e-02
## ORGANIZATION_TYPEUniversity                       1.550397e-01
## ORGANIZATION_TYPEXNA                             -1.047061e+00
## AMR_REQ_CREDIT_BUREAU_SUM1                        2.209506e-02
## AMR_REQ_CREDIT_BUREAU_SUM2                        3.025904e-02
## AMR_REQ_CREDIT_BUREAU_SUM3                        3.363044e-02
## AMR_REQ_CREDIT_BUREAU_SUM4                        2.596637e-02
## AMR_REQ_CREDIT_BUREAU_SUM5                        1.971981e-02
## AMR_REQ_CREDIT_BUREAU_SUM6                        5.909404e-02
## AMR_REQ_CREDIT_BUREAU_SUM7                        5.317304e-02
## AMR_REQ_CREDIT_BUREAU_SUM8                        1.123533e-01
## AMR_REQ_CREDIT_BUREAU_SUM9                        8.795301e-02
## AMR_REQ_CREDIT_BUREAU_SUM10                       6.909664e-02
## AMR_REQ_CREDIT_BUREAU_SUM11                       1.404227e-01
## AMR_REQ_CREDIT_BUREAU_SUM12                       1.658377e-01
## AMR_REQ_CREDIT_BUREAU_SUM13                       1.533711e-01
## AMR_REQ_CREDIT_BUREAU_SUM14                       3.352075e-01
## AMR_REQ_CREDIT_BUREAU_SUM15                       1.760994e-01
## AMR_REQ_CREDIT_BUREAU_SUM16                       5.099801e-01
## AMR_REQ_CREDIT_BUREAU_SUM17                       4.351645e-01
## AMR_REQ_CREDIT_BUREAU_SUM18                       2.872795e-01
## AMR_REQ_CREDIT_BUREAU_SUM19                       5.215201e-01
## AMR_REQ_CREDIT_BUREAU_SUM20                       4.516868e-01
## AMR_REQ_CREDIT_BUREAU_SUM28                       3.977509e-01

Use completed datasets to see AUCs for the fitted models

#Dataset 1
par(mfrow=c(1,1))
cd1 <- mice::complete(application_MI, 1)
reg_cd1 <- glm(data=cd1, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY
               + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE 
               + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS 
               + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM, 
               family=binomial)
roc(cd1$TARGET, fitted(reg_cd1), plot=T, legacy.axes=T)

## 
## Call:
## roc.default(response = cd1$TARGET, predictor = fitted(reg_cd1),     plot = T, legacy.axes = T)
## 
## Data: fitted(reg_cd1) in 9171 controls (cd1$TARGET 0) < 829 cases (cd1$TARGET 1).
## Area under the curve: 0.6981
#Dataset 2
cd2 <- mice::complete(application_MI, 2)
reg_cd2 <- glm(data=cd2, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY
              + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE 
              + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS 
              + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM, 
              family=binomial)
roc(cd2$TARGET, fitted(reg_cd2), plot=T, legacy.axes=T)

## 
## Call:
## roc.default(response = cd2$TARGET, predictor = fitted(reg_cd2),     plot = T, legacy.axes = T)
## 
## Data: fitted(reg_cd2) in 9171 controls (cd2$TARGET 0) < 829 cases (cd2$TARGET 1).
## Area under the curve: 0.6993
#Dataset 3
cd3 <- mice::complete(application_MI, 3)
reg_cd3 <- glm(data=cd3, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY
               + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE 
               + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS 
               + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM, 
               family=binomial)
roc(cd3$TARGET, fitted(reg_cd3), plot=T, legacy.axes=T)

## 
## Call:
## roc.default(response = cd3$TARGET, predictor = fitted(reg_cd3),     plot = T, legacy.axes = T)
## 
## Data: fitted(reg_cd3) in 9171 controls (cd3$TARGET 0) < 829 cases (cd3$TARGET 1).
## Area under the curve: 0.6986

Model Diagnostics

#Dataset 1
cd1 <- mice::complete(application_MI, 1)
#Binned residual plots
rawresid1 = cd1$TARGET - fitted(reg_cd1)
#continuous variables
binnedplot(x=cd1$AMT_INCOME_TOTAL, y = rawresid1, xlab = "AMT_INCOME_TOTAL", ylab = "Residuals", 
           main = "Binned residuals versus AMT_INCOME_TOTAL")

binnedplot(x=cd1$AMT_CREDIT, y = rawresid1, xlab = "AMT_CREDIT", ylab = "Residuals", 
           main = "Binned residuals versus AMT_CREDIT")

binnedplot(x=cd1$AMT_ANNUITY, y = rawresid1, xlab = "AMT_ANNUITY", ylab = "Residuals", 
           main = "Binned residuals versus AMT_ANNUITY")

binnedplot(x=cd1$REGION_POPULATION_RELATIVE , y = rawresid1, xlab = "REGION_POPULATION_RELATIVE", ylab = "Residuals", 
           main = "Binned residuals versus REGION_POPULATION_RELATIVE")

binnedplot(x=cd1$DAYS_BIRTH , y = rawresid1, xlab = "DAYS_BIRTH", ylab = "Residuals", 
           main = "Binned residuals versus DAYS_BIRTH")

binnedplot(x=cd1$DAYS_EMPLOYED , y = rawresid1, xlab = "DAYS_EMPLOYED", ylab = "Residuals", 
           main = "Binned residuals versus DAYS_EMPLOYED")

binnedplot(x=cd1$OWN_CAR_AGE , y = rawresid1, xlab = "OWN_CAR_AGE", ylab = "Residuals", 
           main = "Binned residuals versus OWN_CAR_AGE")

binnedplot(x=as.numeric(cd1$AMR_REQ_CREDIT_BUREAU_SUM) , y = rawresid1, xlab = "OAMR_REQ_CREDIT_BUREAU_SUM", ylab = "Residuals", main = "Binned residuals versus AMR_REQ_CREDIT_BUREAU_SUM")

#DAYS_EMPLOYED
temp <- cd1[cd1$DAYS_EMPLOYED != 365243,]
binnedplot(x=temp$DAYS_EMPLOYED , y = rawresid1, xlab = "DAYS_EMPLOYED", ylab = "Residuals", 
           main = "Binned residuals versus DAYS_EMPLOYED")

Interaction Effect

There are several pairs of variables we would like to check for possible interaction effects.

  • NAME_INCOME_TYPE versus AMT_CREDIT The amount of credit a client is given is typically based on his income type. For example, state servants are considered more stable and banks typically give them higher amounts of credits. Therefore, we believe there could be an interaction effect between these two variables. However, after taking an F-test, we could conclude that the interaction effect is not significant and we should not incorporate into our model.
bwplot(as.factor(TARGET)~AMT_CREDIT|as.factor(NAME_INCOME_TYPE), data = cd1, ylab = "TARGET")

reg_cd1 <- glm(data=cd1, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM, family=binomial)
reg_int <- glm(data=cd1, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM + AMT_CREDIT*NAME_INCOME_TYPE, family=binomial)
anova(reg_cd1, reg_int, test = "Chisq")
## Analysis of Deviance Table
## 
## Model 1: TARGET ~ CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + 
##     FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + 
##     NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + 
##     NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + 
##     DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + 
##     CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM
## Model 2: TARGET ~ CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + 
##     FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + 
##     NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + 
##     NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + 
##     DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + 
##     CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM + 
##     AMT_CREDIT * NAME_INCOME_TYPE
##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1      9863     5318.2                     
## 2      9860     5316.5  3   1.6452   0.6492
  • NAME_EDUCATION_TYPE versus AMT_INCOME_TOTAL The amount of income is typically associated with the highest education he or her received. Therefore, we try to see if there’s any interaction effect between these two variables. The plots below suggest that their might be an interaction effect, though not very clear. But the F-test indicates that we should not add an interaction term between these two variables in our regression model.
bwplot(as.factor(TARGET)~AMT_INCOME_TOTAL|as.factor(NAME_EDUCATION_TYPE), data = cd1, ylab = "TARGET")

reg_cd1 <- glm(data=cd1, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM, family=binomial)
reg_int <- glm(data=cd1, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM + AMT_INCOME_TOTAL*NAME_EDUCATION_TYPE, family=binomial)
anova(reg_cd1, reg_int, test = "Chisq")
## Analysis of Deviance Table
## 
## Model 1: TARGET ~ CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + 
##     FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + 
##     NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + 
##     NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + 
##     DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + 
##     CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM
## Model 2: TARGET ~ CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + 
##     FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + 
##     NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + 
##     NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + 
##     DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + 
##     CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM + 
##     AMT_INCOME_TOTAL * NAME_EDUCATION_TYPE
##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1      9863     5318.2                     
## 2      9859     5314.8  4   3.4027   0.4928
  • OCCUPATION_TYPE versus AMT_INCOME_TOTAL Different kinds of occupation generally have different levels of incomes. For example, high skill tech staffs typically have higher incomes than cleaning staffs. Hence, we think there might be an interaction effect. However, the F-test suggests that the interaction effect is not significant enough and we should not add it into our model.
bwplot(as.factor(TARGET)~AMT_INCOME_TOTAL|as.factor(OCCUPATION_TYPE), data = cd1, ylab = "TARGET")

reg_cd1 <- glm(data=cd1, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM, family=binomial)
reg_int <- glm(data=cd1, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM + AMT_INCOME_TOTAL*OCCUPATION_TYPE, family=binomial)
anova(reg_cd1, reg_int, test = "Chisq")
## Analysis of Deviance Table
## 
## Model 1: TARGET ~ CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + 
##     FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + 
##     NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + 
##     NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + 
##     DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + 
##     CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM
## Model 2: TARGET ~ CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + 
##     FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + 
##     NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + 
##     NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + 
##     DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + 
##     CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM + 
##     AMT_INCOME_TOTAL * OCCUPATION_TYPE
##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1      9863     5318.2                     
## 2      9845     5294.4 18   23.756   0.1632
  • ORGANIZATION_TYPE versus AMT_CREDIT For most banks’ practices, banks generally offer different credit amounts for people work in different organization. For example, most banks offer Fortune 500 companies’ employees more credits. As a result, we think there might be an interaction effect between these two variables. The plots below also suggests that there could be a potential interaction term to be added. However, the F-test indicates that the interaction term is not significant enough to be added into our model.
bwplot(as.factor(TARGET)~AMT_CREDIT|as.factor(ORGANIZATION_TYPE), data = cd1, ylab = "TARGET")

reg_cd1 <- glm(data=cd1, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM, family=binomial)
reg_int <- glm(data=cd1, TARGET~CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM + AMT_CREDIT*ORGANIZATION_TYPE, family=binomial)
anova(reg_cd1, reg_int, test = "Chisq")
## Analysis of Deviance Table
## 
## Model 1: TARGET ~ CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + 
##     FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + 
##     NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + 
##     NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + 
##     DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + 
##     CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM
## Model 2: TARGET ~ CODE_GENDER + NAME_CONTRACT_TYPE + AMT_INCOME_TOTAL + 
##     FLAG_OWN_CAR + FLAG_OWN_REALTY + AMT_CREDIT + AMT_ANNUITY + 
##     NAME_TYPE_SUITE + NAME_INCOME_TYPE + NAME_EDUCATION_TYPE + 
##     NAME_FAMILY_STATUS + NAME_HOUSING_TYPE + REGION_POPULATION_RELATIVE + 
##     DAYS_BIRTH + DAYS_EMPLOYED + OWN_CAR_AGE + OCCUPATION_TYPE + 
##     CNT_FAM_MEMBERS + ORGANIZATION_TYPE + AMR_REQ_CREDIT_BUREAU_SUM + 
##     AMT_CREDIT * ORGANIZATION_TYPE
##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1      9863     5318.2                     
## 2      9809     5276.7 54   41.466   0.8942

Model Interpretation